# Best MLOps Platforms

## How Many MLOps Platforms Products Does G2 Track?

**Total Products under this Category:** 263

### Category Stats (Aug 2026)

- **Average Rating:** 4.5/5 (↓0.01 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Arize AI (+1.02%) - Among all products in this category, Arize AI recorded the largest rating increase compared to last month

_Last updated: August 01, 2026_

## How Does G2 Rank MLOps Platforms Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 7,600+ Authentic Reviews
- 263+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for MLOps Platforms
 ![G2 Grid® for MLOps Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/mlops-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1333204&focus%5B%5D=52115&focus%5B%5D=1308795&focus%5B%5D=125020&focus%5B%5D=10938&focus%5B%5D=1327283)

Highlighted products: Databricks, Gemini Enterprise Agent Platform, Microsoft Fabric, Amazon SageMaker, IBM watsonx.ai, Roboflow, Snowflake, and SAS Viya.

Underlying data: [Grid® JSON](https://www.g2.com/categories/mlops-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=microsoft-fabric&focus%5B%5D=amazon-sagemaker&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=roboflow&focus%5B%5D=snowflake&focus%5B%5D=sas-sas-viya)

**Sponsored**

### SAP Business Data Cloud

SAP Business Data Cloud is a fully managed software-as-a-service (SaaS) solution that unifies and governs SAP data and connects with third-party data. As an evolution of the company's data, planning, and analytics solutions, SAP Business Data Cloud brings together SAP Datasphere, SAP Analytics Cloud, and SAP Business Warehouse with a unified experience that delivers insights across all lines of business. In addition, SAP Databricks is natively available in Business Data Cloud - bringing the power of Databricks Data Intelligence Platform capabilities to the product. SAP Business Data Cloud connects data by leveraging business data fabric principles, making it easier to discover, share, govern, and model this data. It includes SAP Databricks as a first-party data service. The platform combines prebuilt applications and data products across all lines of business. It provides fully managed, curated data products across all lines of business and eliminate the costs of data extracts. Users can build on SAP’s curated data products with their domain expertise, and deliver Intelligent Applications through the Business Data Cloud ecosystem. These intelligent applications are adaptive, AI-powered applications that learn from your data, understand business context, and act on your behalf to transform business outcomes.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=1910&secure%5Bchosen_at%5D=2026-08-01T18%3A49%3A29Z&secure%5Bdisplayable_resource_id%5D=1910&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1910&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1434713&secure%5Bresource_id%5D=1910&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmlops-platforms&secure%5Btoken%5D=ec1bbf0ece7106ddd227b7ec7dcc608a77456df3ad6da47568868fec948b3aa9&secure%5Burl%5D=https%3A%2F%2Fwww.sap.com%2Fdocuments%2F2026%2F03%2Fbc3a8c27-447f-0010-bca6-c68f7e60039b.rc.html%3Fcampaigncode%3DCRM-YD25-BDC-406625%26source%3DBDC-click-campaign-G2&secure%5Burl_type%5D=custom_url)

### [Databricks](https://www.g2.com/pt/products/databricks/reviews)

Databricks é uma plataforma unificada de dados e IA que ajuda as organizações a construir, governar e escalar pipelines de dados, análises, aprendizado de máquina, aplicações de IA e agentes. Mais de 20.000 organizações em todo o mundo — incluindo adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever e 70% das empresas da Fortune 500 — confiam no Databricks para trabalhar com dados empresariais e IA em escala. Com sede em São Francisco e mais de 30 escritórios ao redor do mundo, o Databricks oferece uma plataforma unificada que inclui Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie e Unity Catalog. Fundado em 2013 pelos criadores originais do Apache Spark™, Delta Lake, MLflow e Unity Catalog, o Databricks é construído em uma arquitetura de lakehouse aberta que reúne dados, análises e IA. A plataforma é usada por engenheiros de dados, cientistas de dados, analistas, desenvolvedores, equipes de aprendizado de máquina, equipes de IA e usuários de negócios para colaborar em todo o ciclo de vida de dados e IA. As principais capacidades do Databricks incluem: - Engenharia de dados: Construa, automatize e gerencie pipelines de dados em lote, streaming e em tempo real de forma confiável. - Análise e inteligência de negócios: Execute análises SQL, crie dashboards e permita que as equipes de negócios explorem dados. - Governança de dados: Descubra, proteja e gerencie dados e ativos de IA entre equipes, nuvens e cargas de trabalho. - Aprendizado de máquina e IA: Desenvolva modelos, construa aplicações de IA generativa e crie agentes de IA em nível de produção. - Aplicações de dados: Construa e implante aplicações orientadas a dados usando dados empresariais governados. Disponível em AWS, Azure e Google Cloud, o Databricks ajuda as organizações a trabalhar entre nuvens, reduzir silos de dados e simplificar a colaboração entre equipes e ferramentas. Os clientes usam o Databricks para casos de uso como personalização de clientes, detecção de fraudes, manutenção preditiva, análises em tempo real, cibersegurança, pesquisa em saúde, gestão de risco financeiro, otimização da cadeia de suprimentos e tomada de decisão impulsionada por IA. O Databricks é usado em diversos setores, incluindo serviços financeiros, saúde e ciências da vida, varejo, manufatura, energia e setor público. As organizações usam a plataforma para modernizar a infraestrutura de dados, acelerar a adoção de IA e transformar dados empresariais em valor de negócios.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,327

#### How Do G2 Users Rate Databricks?

- **Facilidade de Uso:** 8.8/10 (Category avg: 8.8/10)
- **Escalabilidade:** 9.0/10 (Category avg: 9.0/10)
- **Métricas:** 8.8/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 8.8/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Databricks?

- **Vendedor:** [Databricks Inc.](https://www.g2.com/pt/sellers/databricks-inc)
- **Website da Empresa:** databricks.com
- **Ano de Fundação:** 2013
- **Localização da Sede:** San Francisco, CA
- **Twitter:** @databricks  
92,269 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bddca64732f61b923d96364e8c8eb35711aab4f98797cb00ab071ff24fbdd392&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3477522%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,627 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Analista de Dados
- **Top Industries:** Tecnologia da Informação e Serviços, Serviços Financeiros
- **Company Size:** 48% Large, 38% Medium

#### What Do G2 Reviewers Say About Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários elogiam a **facilidade de uso** e os **recursos abrangentes** do Databricks para armazenamento de dados e aplicações de ML.
- Os usuários elogiam a **facilidade de uso** do Databricks, melhorando sua experiência com interfaces intuitivas e serviços confiáveis.
- Os usuários apreciam as **integrações perfeitas** do Databricks com a AWS e outras ferramentas, melhorando as operações diárias e a eficiência.
- Os usuários valorizam a **colaboração perfeita** oferecida pelo Databricks, aprimorando o trabalho em equipe em projetos de dados com insights em tempo real.
- Os usuários elogiam os **recursos analíticos integrados** do Databricks, que melhoram o processamento colaborativo de dados e a visualização de insights.

##### Cons

- Os usuários observam uma **curva de aprendizado acentuada** inicialmente, com permissões confusas e modos de computação afetando a usabilidade.
- Os usuários observam que os **custos podem ser bastante altos** para utilizar o Databricks de forma eficaz, especialmente para grandes projetos de dados.
- Os usuários encontram uma **curva de aprendizado íngreme** com o Databricks, especialmente desafiadora para os novatos em ferramentas de big data.
- Os usuários acham a **complexidade** do Databricks desafiadora, especialmente para equipes menores e processos de configuração inicial.
- Os usuários enfrentam desafios de **configuração complexa** inicialmente, embora o suporte ajude a simplificar a experiência ao longo do tempo.

#### What Are Recent G2 Reviews of Databricks?

**["Databricks simplifica ETL e análises com notebooks escaláveis"](https://www.g2.com/pt/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

[Read full review](https://www.g2.com/pt/survey_responses/databricks-review-13181721)

**["Útil para Gerenciar e Analisar Dados Operacionais"](https://www.g2.com/pt/survey_responses/databricks-review-13090803)**

**Rating:** 4.5/5.0 stars

_— Vishaka C._

[Read full review](https://www.g2.com/pt/survey_responses/databricks-review-13090803)

#### What Are G2 Users Discussing About Databricks?

- [What does Databricks software do?](https://www.g2.com/pt/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [O que é a plataforma de análise unificada da Databricks?](https://www.g2.com/pt/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [O que é Lakehouse no Databricks?](https://www.g2.com/pt/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes
- [Quais são os recursos do Databricks?](https://www.g2.com/pt/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

### [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)

Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 654

#### How Do G2 Users Rate Gemini Enterprise Agent Platform?

- **Ease of Use:** 8.2/10 (Category avg: 8.8/10)
- **Scalability:** 8.8/10 (Category avg: 9.0/10)
- **Metrics:** 8.2/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Gemini Enterprise Agent Platform?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, Data Scientist
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 42% Small, 31% Large

#### What Do G2 Reviewers Say About Gemini Enterprise Agent Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of Gemini Enterprise Agent Platform, enhancing productivity and streamlining workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity and streamlining machine learning workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity through reduced manual work in projects.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity by streamlining various tasks and processes.
- Users value the **integrated platform** of Gemini, enhancing productivity by combining various functionalities in a unified system.

##### Cons

- Users find the **pricing ambiguous** with unexpected costs, making budget management a challenge on the Gemini platform.
- Users find the platform's **complexity** ,particularly in navigation and advanced features, challenging, especially for beginners.
- The **learning curve is steep** for new users, especially with complex features and pricing transparency issues.
- Users find the **complexity issues** of the Gemini Enterprise Agent Platform lead to high costs and a steep learning curve.
- Users find the **difficult learning** curve of Gemini Enterprise Agent Platform challenging, especially for newcomers to Google Cloud.

#### What Are Recent G2 Reviews of Gemini Enterprise Agent Platform?

**["Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)**

**Rating:** 5.0/5.0 stars

_— Danyal A._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)

**["Seamless Google Suite Integration for Everyday Work"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)**

**Rating:** 4.5/5.0 stars

_— Shubham S._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)

#### What Are G2 Users Discussing About Gemini Enterprise Agent Platform?

- [What is Google Cloud AI Platform used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-platform-used-for) - 3 comments, 4 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 3 comments, 4 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/discussions/how-do-i-use-google-cloud-platform-for-machine-learning)
- [Is Google Cloud AI free?](https://www.g2.com/discussions/is-google-cloud-ai-free)
- [What is Google AI platform?](https://www.g2.com/discussions/what-is-google-ai-platform) - 2 comments, 2 upvotes

### [Microsoft Fabric](https://www.g2.com/products/microsoft-fabric/reviews)

Microsoft Fabric is a comprehensive, AI-powered data analytics platform that unifies various data management and analysis tools into a single, integrated environment. It combines the capabilities of Microsoft Power BI, Azure Synapse Analytics, and Azure Data Factory, offering a seamless experience for data integration, engineering, warehousing, real-time analytics, data science, and business intelligence. By centralizing these services, Fabric simplifies data management, enhances collaboration, and accelerates the transformation of raw data into actionable insights. Key Features and Functionality: - Unified Data Lake (OneLake): Fabric provides a single, AI-ready data lake that centralizes and curates all business data within a unified, governed hub, ensuring all teams access accurate datasets securely. - AI-Powered Tools: The platform offers AI-enhanced tools tailored for various data projects, enabling teams to innovate faster and derive near real-time insights that drive business impact. - Integrated Analytics Solutions: Fabric encompasses data integration, data engineering, data warehousing, real-time analytics, data science, and business intelligence, all hosted on a lake-centric SaaS solution for simplicity and to maintain a single source of truth. - Built-in Security and Governance: With robust data security, governance, and compliance features, Fabric ensures that data is managed responsibly and in accordance with industry standards. Primary Value and User Solutions: Microsoft Fabric addresses the complexities associated with managing disparate data systems by providing a unified platform that streamlines data workflows. It empowers organizations to harness the full potential of their data, facilitating informed decision-making and fostering innovation. By integrating various data services, Fabric reduces operational overhead, enhances productivity, and supports the development of AI-driven solutions, positioning businesses to thrive in a data-centric landscape.

**Average Rating:** 4.7/5.0

**Total Reviews:** 44

#### How Do G2 Users Rate Microsoft Fabric?

- **Ease of Use:** 9.1/10 (Category avg: 8.8/10)
- **Scalability:** 9.3/10 (Category avg: 9.0/10)
- **Metrics:** 8.9/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Microsoft Fabric?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employees on LinkedIn®
- **Ownership:** MSFT

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Insurance
- **Company Size:** 38% Medium, 38% Large

#### What Do G2 Reviewers Say About Microsoft Fabric?

_AI-generated summary from verified user reviews_

##### Pros

- Users find the **ease of use** of Microsoft Fabric exceptional, allowing seamless adoption even without technical experience.
- Users value the **friendly and responsive customer support** that effectively addresses all inquiries and concerns.
- Users find Microsoft Fabric to be **incredibly intuitive** , making data management accessible for everyone in the organization.
- Users find the **easy setup** of Microsoft Fabric makes it accessible for teams with no prior ETL experience.
- Users value the **integration of tools** in Microsoft Fabric, enhancing usability and offering extensive features for efficiency.

##### Cons

- Users struggle with **formula limitations** in Microsoft Fabric, as some formulas differ from their familiar Excel environment.
- Users face a significant **learning curve** with Microsoft Fabric, which can hinder new users' experience and efficiency.
- Users find **Excel formula issues** frustrating, causing delays while adjusting to Microsoft Fabric's different formula logic.
- Users find a **steep learning curve** in Microsoft Fabric, which can complicate usage for newcomers to the platform.
- Users find that **training is required** to adjust to Microsoft Fabric's formula differences from Excel, but support is available.

#### What Are Recent G2 Reviews of Microsoft Fabric?

**["Finally got our data stack in one place, but costs need attention"](https://www.g2.com/survey_responses/microsoft-fabric-review-12740895)**

**Rating:** 4.0/5.0 stars

_— rishabh m._

[Read full review](https://www.g2.com/survey_responses/microsoft-fabric-review-12740895)

**["Great platform for data analytics development and workflow management"](https://www.g2.com/survey_responses/microsoft-fabric-review-10981663)**

**Rating:** 4.5/5.0 stars

_— Amr a._

[Read full review](https://www.g2.com/survey_responses/microsoft-fabric-review-10981663)

### [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews)

Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.

**Average Rating:** 4.3/5.0

**Total Reviews:** 54

#### How Do G2 Users Rate Amazon SageMaker?

- **Ease of Use:** 8.4/10 (Category avg: 8.8/10)
- **Scalability:** 9.6/10 (Category avg: 9.0/10)
- **Metrics:** 9.4/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Amazon SageMaker?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 33% Medium, 33% Large

#### What Do G2 Reviewers Say About Amazon SageMaker?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Amazon SageMaker's **ease of use** exceptional, enabling quick adaptation and efficient model training with user-friendly features.
- Users appreciate the **seamless AI integration** of Amazon SageMaker, enhancing the efficiency of the machine learning lifecycle.
- Users appreciate the **superior computing power** of Amazon SageMaker, significantly reducing model training time and enhancing efficiency.
- Users value the **exceptional efficiency** of Amazon SageMaker, significantly reducing model training time and streamlining workflows.
- Users commend the **fast processing** capabilities of Amazon SageMaker, significantly reducing model training time and enhancing usability.

##### Cons

- Users find Amazon SageMaker **expensive** , with complex pricing that leads to unexpected costs for training and deployments.
- Users find the **pricing structure complex** and often face high costs with long training jobs and deployments.
- Users find that the **complexity of pricing** in Amazon SageMaker can lead to unexpected costs and confusion.
- Users note a **steep learning curve** with Amazon SageMaker, particularly for those new to AWS services and setups.
- Users experience a **difficult learning curve** during the initial setup of Amazon SageMaker, which can hinder productivity.

#### What Are Recent G2 Reviews of Amazon SageMaker?

**["End-to-End ML Platform That Streamlines the Full Lifecycle"](https://www.g2.com/survey_responses/amazon-sagemaker-review-13180609)**

**Rating:** 4.5/5.0 stars

_— Atharva P._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-13180609)

**["Fully Managed End-to-End ML in AWS with Powerful Distributed Training"](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)**

**Rating:** 4.0/5.0 stars

_— Hem J._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)

#### What Are G2 Users Discussing About Amazon SageMaker?

- [What is Amazon SageMaker used for?](https://www.g2.com/discussions/what-is-amazon-sagemaker-used-for)
- [Is AWS SageMaker good?](https://www.g2.com/discussions/is-aws-sagemaker-good) - 1 upvote
- [Who uses SageMaker?](https://www.g2.com/discussions/who-uses-sagemaker)
- [How do you use Amazon SageMaker?](https://www.g2.com/discussions/how-do-you-use-amazon-sagemaker)
- [What does Amazon SageMaker do?](https://www.g2.com/discussions/what-does-amazon-sagemaker-do)

### [Roboflow](https://www.g2.com/pt/products/roboflow/reviews)

Roboflow tem tudo o que você precisa para construir e implantar aplicações de visão computacional. Mais de 1.000.000 de usuários de empresas de todos os tamanhos — de startups a empresas públicas — usam a plataforma completa da empresa para coleta, organização, anotação, pré-processamento, treinamento de modelos e implantação de imagens e vídeos. Roboflow fornece ferramentas para cada etapa do ciclo de vida de implantação de visão computacional e se integra com suas soluções existentes para que você possa personalizar seu pipeline para atender às suas necessidades.

**Average Rating:** 4.7/5.0

**Total Reviews:** 155

#### How Do G2 Users Rate Roboflow?

- **Facilidade de Uso:** 9.3/10 (Category avg: 8.8/10)
- **Escalabilidade:** 10.0/10 (Category avg: 9.0/10)
- **Métricas:** 10.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Roboflow?

- **Vendedor:** [Roboflow](https://www.g2.com/pt/sellers/roboflow)
- **Website da Empresa:** roboflow.com
- **Ano de Fundação:** 2019
- **Localização da Sede:** Remote, US
- **Twitter:** @roboflow  
13,577 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=660f87d85fdd82e0f1cecfe2354a16103bb5a6f5508134496575ec655201678c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F36096640&secure%5Burl_type%5D=linkedin_company_website)  
137 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Fundador, Pesquisador
- **Top Industries:** Software de Computador, Pesquisa
- **Company Size:** 78% Small, 14% Medium

#### What Do G2 Reviewers Say About Roboflow?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **interface intuitiva** do Roboflow, permitindo uma anotação eficiente e colaboração perfeita para projetos de visão computacional.
- Os usuários valorizam a **eficiência** do Roboflow, elogiando seu gerenciamento de conjuntos de dados simplificado que economiza tempo e reduz erros.
- Os usuários apreciam a **eficiência de anotação** do Roboflow, desfrutando de uma gestão de conjunto de dados simplificada que economiza tempo e reduz erros.
- Os usuários apreciam o **processo fácil de rotulagem de dados** no Roboflow, que simplifica a anotação e melhora a colaboração em equipe.
- Os usuários apreciam os **recursos poderosos e versáteis** do Roboflow, aprimorando projetos acadêmicos e tarefas de visão computacional.

##### Cons

- Os usuários acham o Roboflow **caro** , especialmente para estudantes, pois recursos chave exigem planos pagos para privacidade e personalização.
- Os usuários observam uma **falta de recursos** para análises avançadas e personalização nos planos de nível inferior no Roboflow.
- Os usuários encontram **funcionalidade limitada** no Roboflow, enfrentando desafios como restrições de recursos e falta de flexibilidade em tarefas avançadas.
- Os usuários enfrentam **problemas de anotação** com o Roboflow, muitas vezes precisando de ajustes manuais extensivos para precisão e eficiência.
- Os usuários acham a **gestão ineficiente de rotulagem** complicada, necessitando de organização manual e carecendo de atalhos para uma conclusão de anotação mais suave.

#### What Are Recent G2 Reviews of Roboflow?

**["Acelera nossa pesquisa agri-CV"](https://www.g2.com/pt/survey_responses/roboflow-review-12692685)**

**Rating:** 5.0/5.0 stars

_— Alexey K._

[Read full review](https://www.g2.com/pt/survey_responses/roboflow-review-12692685)

**["Roboflow torna os projetos de visão computacional fáceis de construir, treinar e implantar"](https://www.g2.com/pt/survey_responses/roboflow-review-12984362)**

**Rating:** 5.0/5.0 stars

_— noah r._

[Read full review](https://www.g2.com/pt/survey_responses/roboflow-review-12984362)

### [IBM watsonx.ai](https://www.g2.com/pt/products/ibm-watsonx-ai/reviews)

Watsonx.ai faz parte da plataforma IBM watsonx que reúne novas capacidades de IA generativa, alimentadas por modelos de base e aprendizado de máquina tradicional em um estúdio poderoso que abrange o ciclo de vida da IA. Com o watsonx.ai, você pode construir, treinar, validar, ajustar e implantar IA generativa, modelos de base e capacidades de aprendizado de máquina com facilidade e construir aplicações de IA em uma fração do tempo com uma fração dos dados.

**Average Rating:** 4.4/5.0

**Total Reviews:** 137

#### How Do G2 Users Rate IBM watsonx.ai?

- **Facilidade de Uso:** 8.8/10 (Category avg: 8.8/10)
- **Escalabilidade:** 8.8/10 (Category avg: 9.0/10)
- **Métricas:** 9.1/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 8.7/10 (Category avg: 8.7/10)

#### Who Is the Company Behind IBM watsonx.ai?

- **Vendedor:** [IBM](https://www.g2.com/pt/sellers/ibm)
- **Website da Empresa:** www.ibm.com
- **Ano de Fundação:** 1911
- **Localização da Sede:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Consultor
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 41% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.ai?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** no IBM watsonx.ai, facilitando uma integração de IA mais rápida e uma gestão eficaz.
- Os usuários apreciam a **variedade de modelos** do IBM watsonx.ai, permitindo treinamento personalizado em modelos existentes para desempenho aprimorado.
- Os usuários apreciam a **integração perfeita de IA de nível empresarial** no IBM watsonx.ai, melhorando a tomada de decisões e a eficiência do fluxo de trabalho.
- Os usuários apreciam o **estúdio integrado de nível empresarial** do IBM watsonx.ai para treinamento de IA sem interrupções e insights confiáveis.
- Os usuários valorizam a **integração de IA de nível empresarial** do IBM watsonx.ai, melhorando a tomada de decisões e as operações comerciais de forma eficiente.

##### Cons

- Os usuários acham a **curva de aprendizado difícil** do IBM watsonx.ai intimidadora, tornando-o menos acessível para iniciantes e equipes menores.
- Os usuários acham a **configuração complexa** do IBM watsonx.ai desafiadora, tornando-o menos adequado para pequenas equipes e iniciantes.
- Os usuários acham a **curva de aprendizado íngreme** do IBM watsonx.ai desafiadora, tornando-o menos acessível para equipes não técnicas.
- Os usuários acham o produto **caro** e desafiador para pequenas equipes, citando altos custos e requisitos de configuração complexos.
- Os usuários acham a **configuração complexa** do IBM watsonx.ai desafiadora, especialmente para iniciantes e pequenas equipes.

#### What Are Recent G2 Reviews of IBM watsonx.ai?

**["Estúdio de IA Unificado e Governado com Forte Desempenho e Integrações IBM Sem Costura"](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13184421)**

**Rating:** 4.0/5.0 stars

_— Manan S._

[Read full review](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13184421)

**["Impulsione a produtividade com insights de IA empresarial"](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13191468)**

**Rating:** 4.5/5.0 stars

_— suyog k._

[Read full review](https://www.g2.com/pt/survey_responses/ibm-watsonx-ai-review-13191468)

### [Snowflake](https://www.g2.com/pt/products/snowflake/reviews)

A Snowflake permite que todas as organizações mobilizem seus dados com o AI Data Cloud da Snowflake. Os clientes usam o AI Data Cloud para unir dados isolados, descobrir e compartilhar dados com segurança, alimentar aplicativos de dados e executar diversas cargas de trabalho de IA/ML e analíticas. Onde quer que os dados ou usuários estejam, a Snowflake oferece uma experiência de dados única que abrange várias nuvens e geografias. Milhares de clientes em muitos setores, incluindo 691 dos 2000 maiores do mundo segundo a Forbes em 2023 (G2K) até 31 de janeiro, usam o AI Data Cloud da Snowflake para impulsionar seus negócios.

**Average Rating:** 4.5/5.0

**Total Reviews:** 712

#### How Do G2 Users Rate Snowflake?

- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Escalabilidade:** 9.4/10 (Category avg: 9.0/10)
- **Métricas:** 8.9/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 9.5/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Snowflake?

- **Vendedor:** [Snowflake, Inc.](https://www.g2.com/pt/sellers/snowflake-inc)
- **Website da Empresa:** www.snowflake.com
- **Ano de Fundação:** 2012
- **Localização da Sede:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ad18ff73a9b8bb34dd1b98a6ba1c6be57f7364939ad352612ecc483aba05d2b2&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsnowflake-computing%2F&secure%5Burl_type%5D=linkedin_company_website)  
11,308 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Analista de Dados
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 45% Medium, 43% Large

#### What Do G2 Reviewers Say About Snowflake?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Snowflake, achando-o rápido e eficaz para compartilhamento de dados e análises.
- Os usuários valorizam os **recursos confiáveis** do Snowflake, apreciando sua interface intuitiva e integração de dados perfeita para análises.
- Os usuários acham as **capacidades de gerenciamento de dados** do Snowflake excelentes para agregar e consultar eficientemente em vários conjuntos de dados.
- Os usuários admiram a **escalabilidade perfeita** do Snowflake, acomodando sem esforço as demandas de trabalho e garantindo desempenho ideal.
- Os usuários apreciam a **rápida análise de dados** do Snowflake, permitindo insights rápidos sem preocupações com infraestrutura.

##### Cons

- Os usuários acham os **altos custos** do Snowflake onerosos, especialmente para pequenas empresas com orçamentos limitados.
- Os usuários encontram **limitações de recursos** no Snowflake, como a falta de blocos de código e desafios na gestão de permissões.
- Os usuários descobrem que o **gerenciamento de custos** requer disciplina, pois cobranças inesperadas podem se acumular rapidamente sem monitoramento cuidadoso.
- Os usuários acham a **estrutura de custos difícil de otimizar** , levando a despesas iniciais inesperadamente altas durante a implementação.
- Os usuários acham que os **recursos limitados** do Snowflake em scripts dinâmicos e monitoramento prejudicam a flexibilidade e a usabilidade.

#### What Are Recent G2 Reviews of Snowflake?

**["Escalonamento Elástico e Análises Rápidas com Snowflake"](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)

**["Snowflake Simplifica o Gerenciamento de Dados em Escala"](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

[Read full review](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)

#### What Are G2 Users Discussing About Snowflake?

- [What is Snowflake used for?](https://www.g2.com/pt/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote

### [SAS Viya](https://www.g2.com/pt/products/sas-sas-viya/reviews)

O SAS Viya é uma plataforma de dados e IA nativa da nuvem que permite às equipes construir, implantar e escalar IA explicável que impulsiona decisões confiáveis e seguras. Ele une todo o ciclo de vida de dados e IA e capacita as equipes a inovar rapidamente, equilibrando velocidade, automação e governança por design. O Viya unifica gestão de dados, análises avançadas e tomada de decisão em uma única plataforma, para que as organizações possam passar da experimentação para a produção com confiança, entregando impacto comercial mensurável que é seguro, explicável e escalável em qualquer ambiente. As principais capacidades necessárias para entregar decisões confiáveis incluem: • Clareza de ponta a ponta em todo o ciclo de vida de dados e IA, com linhagem embutida, auditabilidade e monitoramento contínuo para apoiar decisões defensáveis. • Governança por design, permitindo supervisão consistente em dados, modelos e decisões para reduzir riscos e acelerar a adoção. • IA explicável em escala, para que insights e resultados possam ser compreendidos, validados e confiáveis tanto por empresas quanto por reguladores. • Análises operacionalizadas, garantindo que o valor continue além da implantação através de monitoramento, re-treinamento e gestão do ciclo de vida. • Implantação flexível e nativa da nuvem, permitindo que as organizações comecem em qualquer lugar e escalem em todos os lugares enquanto mantêm o controle.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

#### How Do G2 Users Rate SAS Viya?

- **Facilidade de Uso:** 8.2/10 (Category avg: 8.8/10)
- **Escalabilidade:** 8.2/10 (Category avg: 9.0/10)
- **Métricas:** 8.7/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 8.5/10 (Category avg: 8.7/10)

#### Who Is the Company Behind SAS Viya?

- **Vendedor:** [SAS Institute Inc.](https://www.g2.com/pt/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Website da Empresa:** www.sas.com
- **Ano de Fundação:** 1976
- **Localização da Sede:** Cary, NC
- **Twitter:** @SASsoftware  
60,863 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=64db42c044af5bbad79bd9677a620a6c31a8ff1abf4e7b2a6f1d6ed9561d105d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1491%2F&secure%5Burl_type%5D=linkedin_company_website)  
18,638 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Estudante, Bioestatístico
- **Top Industries:** Farmacêuticos, Bancário
- **Company Size:** 33% Large, 33% Small

#### What Do G2 Reviewers Say About SAS Viya?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários valorizam a **facilidade de uso** do SAS Viya, que simplifica a visualização de dados e melhora a eficiência na tomada de decisões.
- Os usuários valorizam as **capacidades analíticas sofisticadas** do SAS Viya, permitindo fácil implantação e tomada de decisões em tempo real.
- Os usuários apreciam os **métodos analíticos avançados** oferecidos pelo SAS Viya, aprimorando as capacidades de tomada de decisão e análise de dados logísticos.
- Os usuários valorizam as **ferramentas de ciclo de vida de dados de ponta a ponta** do SAS Viya, aprimorando a percepção de negócios e a tomada de decisões estratégicas.
- Os usuários adoram a **interface intuitiva** do SAS Viya, tornando a análise de dados e a implantação de modelos sem esforço para todos os níveis de habilidade.

##### Cons

- Os usuários acham que o SAS Viya tem uma **dificuldade de aprendizado** , tornando desafiador para indivíduos não técnicos navegarem de forma eficaz.
- Os usuários acham a **curva de aprendizado íngreme** , tornando desafiador para usuários não técnicos navegarem no SAS Viya de forma eficaz.
- Os usuários acham a **complexidade da visualização** no SAS Viya desafiadora, especialmente para usuários não técnicos e iniciantes.
- Os usuários enfrentam dificuldades com a **curva de aprendizado difícil** do SAS Viya, especialmente para usuários novos e não técnicos.
- Os usuários consideram o **preço elevado** do SAS Viya uma barreira significativa para a adoção potencial.

#### What Are Recent G2 Reviews of SAS Viya?

**["Análise de Dados Eficaz com SAS Viya"](https://www.g2.com/pt/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

[Read full review](https://www.g2.com/pt/survey_responses/sas-viya-review-11872818)

**["SAS Viya: Poderosa IA e Análise de Dados com Integrações Sem Costura"](https://www.g2.com/pt/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Usuário Verificado em Hospital e Cuidados de Saúde_

[Read full review](https://www.g2.com/pt/survey_responses/sas-viya-review-11855145)

#### What Are G2 Users Discussing About SAS Viya?

- [Para que é usado o SAS Visual Data Mining and Machine Learning?](https://www.g2.com/pt/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 2 comments

### [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews)

Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 87

#### How Do G2 Users Rate Azure Machine Learning?

- **Ease of Use:** 8.5/10 (Category avg: 8.8/10)
- **Scalability:** 9.2/10 (Category avg: 9.0/10)
- **Metrics:** 8.3/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.2/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Azure Machine Learning?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employees on LinkedIn®
- **Ownership:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Large, 33% Small

#### What Do G2 Reviewers Say About Azure Machine Learning?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Azure Machine Learning to be **easy to use** , facilitating seamless data management and model implementation.
- Users appreciate the **scalability and integration** of Azure Machine Learning, enhancing AI deployment across various applications.
- Users appreciate the **excellent customer support** of Azure Machine Learning, with helpful documentation and community assistance available.
- Users appreciate the **ease of use and rich features** of Azure Machine Learning for effective data management.
- Users appreciate the **efficiency** of Azure Machine Learning for launching and monitoring jobs seamlessly, enhancing productivity.

##### Cons

- Users find the **learning curve challenging** , requiring time and effort to navigate the platform's tools effectively.
- Users find Azure Machine Learning's **difficult navigation** frustrating due to its disordered interface and non-intuitive workflows.
- Users find the **user interface disorganized** , leading to confusion and excessive clicking to locate options.
- Users find the **complex interface** of Azure Machine Learning non-intuitive, complicating their workflow and experience.
- Users face a **difficult learning curve** with Azure Machine Learning, especially if they are new to the platform.

#### What Are Recent G2 Reviews of Azure Machine Learning?

**["Cost-Efficient Medical Data Integration Backed by Great Support"](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)**

**Rating:** 5.0/5.0 stars

_— Giridharan U._

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)

**["An Enterprise-Grade Way to Operationalize ML"](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)**

**Rating:** 4.0/5.0 stars

_— Vytas J._

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)

#### What Are G2 Users Discussing About Azure Machine Learning?

- [What is Azure Machine Learning Studio used for?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio-used-for) - 1 comment
- [What type of data analysis is azure machine learning studio intended for?](https://www.g2.com/discussions/what-type-of-data-analysis-is-azure-machine-learning-studio-intended-for)
- [What are the key features of Azure Machine Learning?](https://www.g2.com/discussions/what-are-the-key-features-of-azure-machine-learning)
- [How do I use Microsoft Azure for machine learning?](https://www.g2.com/discussions/how-do-i-use-microsoft-azure-for-machine-learning)
- [What is Azure Machine Learning Studio?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio)

### [Dataiku](https://www.g2.com/pt/products/dataiku/reviews)

Dataiku é a Plataforma para o Sucesso em IA: a camada de orquestração de IA onde as empresas constroem, implantam e governam análises, modelos e agentes em escala. Ela se posiciona sobre as plataformas de dados, nuvens e serviços de IA que você já utiliza, funcionando em todos eles sem prendê-lo a nenhum. Dataiku amplia quem pode construir IA de produção, colocando as ferramentas certas nas mãos de cientistas de dados e especialistas de domínio, desde analistas de fraude até planejadores de demanda. Ela orquestra aprendizado de máquina, regras, LLMs e agentes como um sistema governado, construído com base em mais de uma década de execução de IA em produção. A governança é parte da construção, em vez de algo adicionado posteriormente, permitindo que as equipes entreguem mais rápido enquanto mantêm o desempenho, custo e risco sob controle. O resultado: IA que passa da experimentação para uma execução confiável e mensurável agora, não em 18 meses.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **Facilidade de Uso:** 8.7/10 (Category avg: 8.8/10)
- **Escalabilidade:** 9.1/10 (Category avg: 9.0/10)
- **Métricas:** 8.7/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 8.6/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Dataiku?

- **Vendedor:** [Dataiku](https://www.g2.com/pt/sellers/dataiku)
- **Website da Empresa:** Dataiku.com
- **Ano de Fundação:** 2013
- **Localização da Sede:** New York, NY
- **Twitter:** @dataiku  
22,917 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e59ec8fccc02ecc4f883419e54da56d3f6fc8b1e556153f0cc01cd05e3b77faa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataiku%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,619 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Cientista de Dados, Analista de Dados
- **Top Industries:** Serviços Financeiros, Farmacêuticos
- **Company Size:** 60% Large, 22% Medium

#### What Do G2 Reviewers Say About Dataiku?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam como o Dataiku facilita o **desenvolvimento fácil de ML** , permitindo o foco na construção de modelos sem a complexidade.
- Os usuários adoram a **facilidade de uso** no Dataiku, simplificando tarefas complexas e aprimorando sua experiência de análise de dados.
- Os usuários apreciam a **facilidade de uso** no Dataiku, permitindo a colaboração tanto para usuários técnicos quanto não técnicos.
- Os usuários apreciam as **integrações fáceis** do Dataiku, facilitando a colaboração e implantação suave entre várias ferramentas de análise.
- Os usuários se beneficiam da **melhoria de produtividade** do Dataiku, permitindo um desenvolvimento de projetos mais rápido e um crescimento profissional aprimorado.

##### Cons

- Os usuários acham a **curva de aprendizado íngreme** do Dataiku desafiadora, tornando difícil para os iniciantes dominarem a plataforma.
- Os usuários acham a **curva de aprendizado íngreme** desafiadora para iniciantes, impactando sua capacidade de usar o Dataiku de forma eficaz.
- Os usuários acham a **curva de aprendizado difícil** desafiadora, especialmente para iniciantes que navegam por recursos avançados.
- Os usuários experimentam **desempenho lento** com o Dataiku ao lidar com grandes conjuntos de dados, afetando a eficiência e a produtividade.
- Os usuários acham o Dataiku **caro** , especialmente para organizações e projetos menores, impactando a acessibilidade e a acessibilidade econômica.

#### What Are Recent G2 Reviews of Dataiku?

**["Plataforma Unificada de Baixo Código que Aumenta a Produtividade de Dados e IA de Ponta a Ponta"](https://www.g2.com/pt/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/dataiku-review-13125252)

**["Construa fluxos de trabalho mais rápidos com dados conectados de muitos provedores ou fontes de dados distintas"](https://www.g2.com/pt/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/pt/survey_responses/dataiku-review-13120436)

#### What Are G2 Users Discussing About Dataiku?

- [Is Dataiku an ETL tool?](https://www.g2.com/pt/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/pt/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/pt/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/pt/discussions/what-is-dataiku-dss-used-for)

### [Weights & Biases](https://www.g2.com/products/weights-biases/reviews)

Weights & Biases is the AI developer platform to build AI applications and models with confidence. ML engineers and AI developers use W&B Weave and W&B Models to coordinate all LLMops and MLops processes, including evaluating, debugging, training, fine-tuning, and deploying. W&B Weave helps developers evaluate, monitor and iterate on their AI applications to continuously improve quality, latency, cost, and safety. W&B Models boosts experiment speed and team collaboration among ML teams, helping them bring models to production faster while ensuring performance, data reliability, and security. W&B also serves as the system of record for all ML and AI activities.

**Average Rating:** 4.6/5.0

**Total Reviews:** 52

#### How Do G2 Users Rate Weights & Biases?

- **Ease of Use:** 8.7/10 (Category avg: 8.8/10)
- **Scalability:** 8.3/10 (Category avg: 9.0/10)
- **Metrics:** 9.0/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.6/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Weights & Biases?

- **Seller:** [CoreWeave](https://www.g2.com/sellers/coreweave)
- **Year Founded:** 2017
- **HQ Location:** New York, US
- **Twitter:** @CoreWeave  
23,758 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8dc6fb72f750b09440d04ea4332dc85d5858c061be8cb5e2539ff9987d0c7aee&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcoreweave%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,289 employees on LinkedIn®
- **Ownership:** NASDAQ:CRWV

#### Who Uses This Product?

- **Top Industries:** Computer Software, Research
- **Company Size:** 51% Small, 30% Medium

#### What Do G2 Reviewers Say About Weights & Biases?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of Weights & Biases, simplifying tracking and sharing experiments effortlessly.
- Users praise the **seamless integration** of Weights & Biases with libraries, enhancing collaboration and simplifying experiment management.
- Users value the **easy setup** of Weights & Biases, enhancing productivity and simplifying collaboration across multiple platforms.
- Users appreciate the **fast and experienced customer support** of Weights & Biases, enhancing their overall experience.
- Users appreciate the **customization flexibility** of Weights & Biases for logging parameters and visualizing model comparisons.

##### Cons

- Users are often frustrated by the **insufficient documentation for basic functionalities** in Weights & Biases.
- Users find the **lack of guidance** in documentation frustrating, especially when seeking basic functionalities in Weights & Biases.
- Users find a **lack of tools** for easily discarding non-useful runs, complicating their workflow with Weights & Biases.
- Users desire **additional features** like global normalization settings and better control over window management on reload.
- Users find the **poor documentation** frustrating, especially when seeking basic functionalities of Weights & Biases.

#### What Are Recent G2 Reviews of Weights & Biases?

**["Essential ML Experiment Tracking with Real-Time Metrics and Team Collaboration"](https://www.g2.com/survey_responses/weights-biases-review-13193236)**

**Rating:** 4.5/5.0 stars

_— Muhammed A._

[Read full review](https://www.g2.com/survey_responses/weights-biases-review-13193236)

**["A Must-Have Tool for Keeping ML Experiments Organized"](https://www.g2.com/survey_responses/weights-biases-review-13174389)**

**Rating:** 4.0/5.0 stars

_— Jeni J._

[Read full review](https://www.g2.com/survey_responses/weights-biases-review-13174389)

#### What Are G2 Users Discussing About Weights & Biases?

- [What is Weights & Biases used for?](https://www.g2.com/discussions/what-is-weights-biases-used-for)

### [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)

SuperAnnotate bridges the gap between cutting-edge AI innovation and the high-quality human data that powers it - helping advanced AI teams build more intelligent models. With a global network of thousands of rigorously vetted experts, ethical and scalable managed operations, precise talent matching, and purpose‑built technology, SuperAnnotate delivers full project visibility and unmatched data quality. SuperAnnotate powers complex annotation, evaluation, and reinforcement learning workflows to build, evaluate and align frontier AI. Trusted by innovators like Databricks, IBM and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate enables the world’s top AI teams to build responsible and state‑of‑the‑art models with human data.

**Average Rating:** 4.8/5.0

**Total Reviews:** 353

#### How Do G2 Users Rate SuperAnnotate?

- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)
- **Scalability:** 9.9/10 (Category avg: 9.0/10)
- **Metrics:** 9.7/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.9/10 (Category avg: 8.7/10)

#### Who Is the Company Behind SuperAnnotate?

- **Seller:** [SuperAnnotate](https://www.g2.com/sellers/superannotate)
- **Company Website:** superannotate.com
- **Year Founded:** 2018
- **HQ Location:** San Francisco, CA
- **Twitter:** @superannotate  
720 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ed4d3a394b0ca2eaac6c63041f9bd1bf14ee26538d356cd977a2b0f50c15f4d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F18999422%2F&secure%5Burl_type%5D=linkedin_company_website)  
361 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Data Trainer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 57% Small, 23% Medium

#### What Do G2 Reviewers Say About SuperAnnotate?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **intuitive interface** of SuperAnnotate, making large-scale annotation projects easy to manage and efficient.
- Users appreciate the **user-friendly interface** of SuperAnnotate, which enhances the efficiency and accuracy of their annotation tasks.
- Users value the **annotation efficiency** of SuperAnnotate, enabling quick, consistent, and high-quality annotations across diverse projects.
- Users commend the **efficiency** of SuperAnnotate, appreciating the time saved and the streamlined annotation process.
- Users value SuperAnnotate for its **high-quality annotations** , ensuring consistent and efficient collaboration and management of projects.

##### Cons

- Users notice **performance issues** with SuperAnnotate, particularly related to loading times and occasional technical glitches.
- Users experience **slow performance** with SuperAnnotate, particularly during image cropping and handling large projects.
- Users find the **difficult learning** curve for advanced features challenging, impacting their overall experience with SuperAnnotate.
- Users find the **complexity of the platform** daunting, particularly for new users navigating advanced features.
- Users find a **lack of guidance** in SuperAnnotate, making it challenging for newcomers to navigate advanced features effectively.

#### What Are Recent G2 Reviews of SuperAnnotate?

**["Streamlines Annotation with an Easy Setup and Strong Support"](https://www.g2.com/survey_responses/superannotate-review-12584940)**

**Rating:** 4.0/5.0 stars

_— Nada A._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-12584940)

**["Easy-to-Use Data Organization and Powerful Image-Splitting Tools"](https://www.g2.com/survey_responses/superannotate-review-13146852)**

**Rating:** 5.0/5.0 stars

_— Doniaa K._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-13146852)

#### What Are G2 Users Discussing About SuperAnnotate?

- [What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?](https://www.g2.com/discussions/what-is-your-experience-with-superannotate-for-data-annotation-and-what-would-you-like-to-see-improved) - 1 comment
- [How do I annotate an image in OpenCV?](https://www.g2.com/discussions/how-do-i-annotate-an-image-in-opencv)
- [Is SuperAnnotate free?](https://www.g2.com/discussions/is-superannotate-free)
- [How do you use SuperAnnotate?](https://www.g2.com/discussions/how-do-you-use-superannotate)
- [What is SuperAnnotate?](https://www.g2.com/discussions/what-is-superannotate) - 1 comment, 2 upvotes

### [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews)

Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs. Key Features: - Pure Python: Workflows are defined using standard Python code, allowing for dynamic pipeline generation and easy integration with existing Python libraries. - User-Friendly Web Interface: A robust web application enables users to monitor, schedule, and manage workflows without the need for command-line interfaces. - Extensibility: Users can define custom operators and extend libraries to fit their specific environment, enhancing the platform's flexibility. - Scalability: Airflow's modular architecture and use of message queues allow it to orchestrate an arbitrary number of workers, making it ready to scale as needed. - Robust Integrations: The platform offers numerous plug-and-play operators for executing tasks across various cloud platforms and third-party services, facilitating easy integration with existing infrastructure. Primary Value and Problem Solving: Apache Airflow addresses the challenges of managing complex data workflows by providing a scalable and dynamic platform for workflow orchestration. By defining workflows as code, it ensures reproducibility, version control, and collaboration among teams. The platform's extensibility and robust integrations allow organizations to adapt it to their specific needs, reducing operational overhead and improving efficiency in data processing tasks. Its user-friendly interface and monitoring capabilities enhance transparency and control over workflows, leading to improved data quality and reliability.

**Average Rating:** 4.4/5.0

**Total Reviews:** 128

#### How Do G2 Users Rate Apache Airflow?

- **Ease of Use:** 8.5/10 (Category avg: 8.8/10)
- **Scalability:** 9.1/10 (Category avg: 9.0/10)
- **Metrics:** 8.5/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 9.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Apache Airflow?

- **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation)
- **Year Founded:** 1999
- **HQ Location:** Wakefield, MA
- **Twitter:** @TheASF  
66,168 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b8484c4bb31e87bc0ba9e683d86f2af14309539343a063507c88bcdcff98434d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F215982%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,470 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 45% Medium, 31% Large

#### What Do G2 Reviewers Say About Apache Airflow?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in Apache Airflow, facilitating efficient workflow creation and monitoring.
- Users appreciate the **intuitive dashboard** of Apache Airflow for effortlessly monitoring workflows and task statuses.
- Users highly value the **flexibility** of Apache Airflow, allowing for customized workflows via Python code.
- Users appreciate the **workflow automation** capabilities of Apache Airflow, simplifying complex data pipeline management.
- Users appreciate the **easy integrations** in Apache Airflow, making it flexible for connecting various systems and tools.

##### Cons

- Users face a **difficult setup** when installing Apache Airflow, especially on Windows systems, complicating the onboarding process.
- Users find the **learning curve challenging** , requiring time to grasp operators and manage workflows effectively.
- Users find the **steep learning curve** of Airflow challenging, especially with concepts and setup complexities.
- Users find the **learning difficulty** of Apache Airflow to be a barrier, particularly with Jinja and job setups.
- Users find the **outdated user interface** of Apache Airflow detracts from an otherwise reliable experience.

#### What Are Recent G2 Reviews of Apache Airflow?

**["Scalable Workflows with Apache Airflow, Best data Engineering tool for Orchestrator,Easy Deployment"](https://www.g2.com/survey_responses/apache-airflow-review-12703177)**

**Rating:** 4.5/5.0 stars

_— Rajesh K._

[Read full review](https://www.g2.com/survey_responses/apache-airflow-review-12703177)

**["Powerful for complex ML pipelines, but comes with a steep infrastructure learning curve"](https://www.g2.com/survey_responses/apache-airflow-review-12935519)**

**Rating:** 5.0/5.0 stars

_— Sachin G._

[Read full review](https://www.g2.com/survey_responses/apache-airflow-review-12935519)

#### What Are G2 Users Discussing About Apache Airflow?

- [What is Apache Airflow used for?](https://www.g2.com/discussions/what-is-apache-airflow-used-for)
- [What is airflow technology?](https://www.g2.com/discussions/what-is-airflow-technology) - 1 comment
- [Is airflow a framework?](https://www.g2.com/discussions/is-airflow-a-framework) - 1 comment
- [Is Apache airflow an ETL tool?](https://www.g2.com/discussions/is-apache-airflow-an-etl-tool) - 1 comment
- [Who is using Apache airflow?](https://www.g2.com/discussions/who-is-using-apache-airflow) - 1 comment

### [JFrog](https://www.g2.com/pt/products/jfrog-2024-03-28/reviews)

A JFrog Ltd. (Nasdaq: FROG), criadora da plataforma unificada de DevOps, DevSecOps, DevGovOps e MLOps, está em uma missão para criar um mundo de software entregue sem atrito do desenvolvimento à produção. Impulsionada por uma visão de "Software Líquido" para manter o software fluindo continuamente, seguro e sempre atualizado, a Plataforma JFrog serve como o sistema definitivo de registro da cadeia de suprimentos de software. Ela é exclusivamente projetada para capacitar organizações a construir, gerenciar e distribuir software confiável com velocidade, segurança e escala sem precedentes em ambientes híbridos e multi-nuvem. À medida que a engenharia de software evolui na era da IA, as ofertas mais recentes da JFrog abordam a tendência mais urgente da indústria: o aumento do desenvolvimento de software agente e os riscos de segurança ocultos da "IA Sombra". Em resposta a atores de ameaças que cada vez mais visam fluxos de trabalho de desenvolvedores, incluindo um aumento maciço em modelos de IA de código aberto maliciosos e pacotes infectados; a JFrog expandiu as capacidades de sua plataforma para oferecer visibilidade absoluta de ponta a ponta e conformidade automatizada. As principais novas inovações incluem o JFrog AI Catalog, que permite às organizações centralizar, governar e controlar o ciclo de vida dos modelos de IA aprovados para uso empresarial. Para proteger ambientes de codificação autônomos, a JFrog introduziu o Universal MCP Registry e o Agent Skills Registry (desenvolvido junto com a NVIDIA). Essas novas soluções estabelecem a primeira camada de confiança de nível empresarial da indústria para gerenciar e armazenar com segurança habilidades de agentes de IA, monitorar conexões e bloquear instantaneamente ferramentas de desenvolvedor inseguras ou extensões de codificação maliciosas diretamente onde os desenvolvedores trabalham. Além disso, a integração de ferramentas avançadas de DevGovOps e Segurança em Tempo de Execução permite que as equipes substituam auditorias de conformidade lentas e manuais por uma aplicação contínua e em segundo plano de políticas. Ao deslocar a segurança para a esquerda diretamente no pipeline binário, a JFrog garante que o volume de código assistido por IA não ultrapasse a capacidade de uma organização de verificar sua segurança. Hoje, milhões de usuários e aproximadamente 6.600 organizações em todo o mundo, incluindo a maioria das empresas da Fortune 100, dependem da Plataforma universal JFrog para eliminar a fadiga de soluções pontuais, fechar a lacuna de governança e adotar com segurança a transformação digital. Saiba mais em www.jfrog.com ou siga-nos no X @JFrog.

**Average Rating:** 4.2/5.0

**Total Reviews:** 149

#### How Do G2 Users Rate JFrog?

- **Facilidade de Uso:** 8.1/10 (Category avg: 8.8/10)
- **Escalabilidade:** 10.0/10 (Category avg: 9.0/10)

#### Who Is the Company Behind JFrog?

- **Vendedor:** [JFrog Ltd](https://www.g2.com/pt/sellers/jfrog-ltd)
- **Website da Empresa:** jfrog.com
- **Ano de Fundação:** 2008
- **Localização da Sede:** Sunnyvale, CA
- **Twitter:** @jfrog  
23,186 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9e9f01c1efeb3f3e7b4535b3aefc16344bbb21773bc11bf4ad186f193dbcaabf&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fjfrog-ltd%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,364 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Software, Engenheiro de DevOps
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 50% Large, 31% Medium

#### What Do G2 Reviewers Say About JFrog?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **integração abrangente e o suporte a múltiplos formatos** do JFrog, otimizando seus processos de DevOps de forma eficaz.
- Os usuários apreciam o **gerenciamento centralizado de artefatos** da JFrog, aumentando a eficiência no armazenamento e rastreamento de componentes em diferentes ambientes.
- Os usuários valorizam a **integração de implantação perfeita** do JFrog, melhorando efetivamente os pipelines de CI/CD e a gestão de segurança.
- Os usuários valorizam as **integrações perfeitas** do JFrog, aprimorando seus processos de CI/CD em vários formatos de pacotes.
- Os usuários valorizam as **integrações fáceis** do JFrog com várias ferramentas, melhorando seus fluxos de trabalho de CI/CD de forma contínua.

##### Cons

- Os usuários acham a plataforma da JFrog **excessivamente complexa** , exigindo treinamento significativo para navegar efetivamente por seus recursos extensos.
- Os usuários acham o JFrog **caro** , com custos que representam desafios para equipes menores e desenvolvedores individuais.
- Os usuários muitas vezes enfrentam uma **curva de aprendizado acentuada** com o JFrog, exigindo tempo significativo para dominar sua complexidade.
- Os usuários acham que a **curva de aprendizado difícil** do JFrog requer treinamento extensivo para navegar efetivamente em seus recursos complexos.
- Os usuários acham que o JFrog tem uma **curva de aprendizado acentuada** , exigindo tempo e esforço significativos para alcançar a proficiência.

#### What Are Recent G2 Reviews of JFrog?

**["Gestão de Artefatos Eficiente e Escalável que Simplifica o Ciclo de Vida de Entrega de Software"](https://www.g2.com/pt/survey_responses/jfrog-review-12788318)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/pt/survey_responses/jfrog-review-12788318)

**["JFrog simplifica o gerenciamento de artefatos para implantações organizadas e confiáveis"](https://www.g2.com/pt/survey_responses/jfrog-review-12870354)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

[Read full review](https://www.g2.com/pt/survey_responses/jfrog-review-12870354)

#### What Are G2 Users Discussing About JFrog?

- [Quais são os benefícios e desafios de usar o JFrog para gerenciar sua cadeia de suprimentos de software?](https://www.g2.com/pt/discussions/what-are-the-benefits-and-challenges-of-using-jfrog-for-managing-your-software-supply-chain)
- [What does Jfrog Platform do?](https://www.g2.com/pt/discussions/what-does-jfrog-platform-do)
- [What is difference between JFrog and Nexus?](https://www.g2.com/pt/discussions/what-is-difference-between-jfrog-and-nexus)
- [What is Artifactory software used for?](https://www.g2.com/pt/discussions/what-is-artifactory-software-used-for)

### [Edge Impulse](https://www.g2.com/products/edge-impulse/reviews)

Edge Impulse is an end-to-end platform for edge AI application development. We enable developers to use their own sensor, audio and vision data to train AI models for classification, regression and anomaly detection. Our platform is hardware-aware and developers can build models that scale from MCUs to NPUs. We support MLOps from start to finish - from initial data collection to monitoring the model in the field.

**Average Rating:** 4.5/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate Edge Impulse?

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 8.1/10 (Category avg: 9.0/10)
- **Metrics:** 8.3/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Edge Impulse?

- **Seller:** [Qualcomm](https://www.g2.com/sellers/qualcomm)
- **Year Founded:** 1985
- **HQ Location:** San Diego, CA
- **Twitter:** @Qualcomm  
441,209 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c011c5355b10e30f99e8c597addd4e1f5e9def722397be57a2defdd1a5140c47&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fqualcomm%2F&secure%5Burl_type%5D=linkedin_company_website)  
56,625 employees on LinkedIn®
- **Ownership:** NASDAQ:QCOM

#### Who Uses This Product?

- **Company Size:** 64% Small, 36% Large

#### What Do G2 Reviewers Say About Edge Impulse?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **extensive data augmentation and deployment options** offered by Edge Impulse for enhanced model performance.
- Users appreciate the **ease of use** of Edge Impulse, finding its interface intuitive for importing and scaling data.
- Users value the **data augmentation and preprocessing tools** that enhance training data quality and improve model performance.
- Users value the **flexibility** of Edge Impulse for deploying models across diverse edge devices and formats.
- Users find Edge Impulse's **user-friendly interface** makes machine learning accessible for various edge devices effortlessly.

##### Cons

- Users feel that the **lack of offline documentation** can hinder their usage of Edge Impulse in low-connectivity areas.
- Users feel that the **lack of tools** limits support for custom embedded devices, hindering broader experimentation.
- Users find the **limited customization** of Edge Impulse restrictive for building complex or specialized machine learning models.
- Users feel that **missing features** for custom embedded devices limit the product's appeal and usability for developers.
- Users find the **model limitations** of Edge Impulse restrictive for complex, specialized applications, craving more customization options.

#### What Are Recent G2 Reviews of Edge Impulse?

**["Using Edge as a fairly new user"](https://www.g2.com/survey_responses/edge-impulse-review-8506732)**

**Rating:** 5.0/5.0 stars

_— Georgian C._

[Read full review](https://www.g2.com/survey_responses/edge-impulse-review-8506732)

**["Empowering Edge AI Innovation: A Comprehensive Edge Impulse Review"](https://www.g2.com/survey_responses/edge-impulse-review-8506779)**

**Rating:** 5.0/5.0 stars

_— Alex G._

[Read full review](https://www.g2.com/survey_responses/edge-impulse-review-8506779)

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[Browse MLOps Platforms Themes](/categories/mlops-platforms/themes)

 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Machine learning operationalization (MLOps) platforms allow users to manage, monitor, and deploy machine learning models as they are integrated into business applications, automating deployment, tracking model health and accuracy, and enabling teams to scale machine learning across the organization for tangible business impact.

### Core Capabilities of MLOps Platforms

To qualify for inclusion in the MLOps Platforms category, a product must:

- Offer a platform to monitor and manage machine learning models
- Allow users to integrate models into business applications across a company
- Track the health and performance of deployed machine learning models
- Provide a holistic management tool to better understand all models deployed across a business

### Common Use Cases for MLOps Platforms

Data science and ML engineering teams use MLOps platforms to operationalize models and maintain their performance over time. Common use cases include:

- Automating the deployment pipeline for ML models built by data scientists into production applications
- Monitoring model drift, accuracy degradation, and performance anomalies in deployed models
- Managing experiment tracking, model versioning, and security governance across the ML lifecycle

### How MLOps Platforms Differ from Other Tools

MLOps platforms focus on the maintenance and monitoring of deployed models rather than initial model development, distinguishing them from [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which focus on model building and training. Some MLOps solutions offer centralized management of all models across the business in a single location, and may be language-agnostic or optimized for specific languages like Python or R.

### Insights from G2 on MLOps Platforms

Based on category trends on G2, model monitoring and experiment tracking stand out as the most valued capabilities. Improved model reliability and faster iteration cycles stand out as primary benefits of adoption.

Top Tools at a Glance

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Unified lakehouse for ML and data engineering

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User Review

"Databricks Streamlines ETL and Analytics with Scalable Notebooks"

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| 

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End-to-end ML lifecycle on Google Cloud

 | 

User Review

"Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform"

 |
| 

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Unified data-to-analytics pipelines inside Microsoft ecosystem

 | 

User Review

"Finally got our data stack in one place, but costs need attention"

 |
| 

 | 

End-to-end ML workflows inside AWS ecosystem

 | 

User Review

"End-to-End ML Platform That Streamlines the Full Lifecycle"

 |
| 

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Computer vision dataset annotation to deployment

 | 

User Review

"Roboflow Makes Computer Vision Projects Easy to Build, Train, and Deploy"

 |
| 

 | 

Enterprise AI governance with foundation model deployment

 | 

User Review

"Boosts Productivity with Enterprise AI Insights"

 |
| 

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ML pipelines on centralized multi-source data

 | 

User Review

"Snowflake Simplifies Data Management at Scale"

 |
| 

 | 

Enterprise ML governance with SAS code continuity

 | 

User Review

"SAS Viya: Powerful AI & Data Analysis with Seamless Integrations"

 |
| 

 | 

Beginner-friendly model deployment with Azure integration

 | 

User Review

"Cost-Efficient Medical Data Integration Backed by Great Support"

 |
| 

 | 

Cross-functional ML workflows with visual and code flexibility

 | 

User Review

"Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"

 |

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Show More

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## How Do You Choose the Right MLOps Platforms?

### What You Should Know About MLOps Platforms

### What are MLOps Platforms?

MLOps solutions apply tools and resources to ensure that machine learning projects are run properly and efficiently, including data governance, model management, and model deployment.

The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With machine learning, users are enabled to mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and helps make data-driven predictions.

One crucial aspect of the machine learning process is the development, management, and monitoring of machine learning models. Users leverage MLOps Platforms to manage and monitor machine learning models as they are integrated into business applications. 

Although MLOps capabilities can come together in software products or platforms, it is fundamentally a methodology. When data scientists, data engineers, developers, and other business stakeholders collaborate and ensure that the data is properly managed and mined for meaning, they need MLOps to ensure that teams are aligned, and that machine learning projects are tracked and can be reproduced.

#### What Types of MLOps Platforms Exist?

Not all MLOps Platforms are created equal. These tools allow developers and data scientists to manage and monitor machine learning models. However, they differ in terms of the data types supported, as well as the method and manner of deployment. 

**Cloud**

With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insights from it as well as to ensure its quality. These platforms allow them to train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models which have been deployed.

**On-premises**

Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for a number of reasons, including data security and latency issues. In cases like health care, strict regulations such as HIPAA require data to be secure. Therefore, on-premises solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes vital.

**Edge**

Some platforms allow for spinning up algorithms on the edge, which consists of a mesh network of data centers that process and store data locally prior to being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** **

### What are the Common Features of MLOps Platforms?

The following are some core features within MLOps Platforms that can be useful to users:

**Model training:** Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and results in improved model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process whereby the proper values are determined for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data.

**Model management:** The process does not end once the model is released. Businesses must monitor and manage their models to ensure they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss. It can help with recording, cataloging, and organizing all machine learning models deployed across the business. Not all models are meant for all users. Therefore, some tools allow for provisioning users based on authorization to both deploy and iterate upon machine learning models.

**Model deployment:** The deployment of machine learning models is the process of making the models available in production environments, where they provide predictions to other software systems. Some tools allow users to manage model artifacts and track which models are deployed in production. Methods of deployments take the form of REST APIs, GUI for on-demand analysis, and more.

**Metrics:** Users can control model usage and performance in production. This helps track how the models are performing.

### What are the Benefits of MLOps Platforms?

Through the use of MLOps Platforms, data scientists can gain visibility into their machine learning endeavors. This helps them better understand what is and isn’t working, and they are provided with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries, and deploy their algorithms into production.

**Share data insights:** Users are enabled to share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork.

**Simplify and scale data science:** Pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms efficiently help scale experiments across many nodes to perform distributed training on large datasets.

**Experiment better:** Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. MLOps Platforms facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for deep learning are also used in experimentation, which are algorithms or methods used to change the attributes of neural networks such as weights and learning rate to reduce the losses.

### Who Uses MLOps Platforms?

Data scientists are in high demand, but there is a shortage in the number of skilled professionals available. The skillset is varied and vast (for example, there is a need to understand a vast array of algorithms, advanced mathematics, programming skills, and more); therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms are increasingly including features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms.

In addition, for data science projects to initiate, it is key that the broader business buys into these projects. The more robust platforms provide resources that give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which have been impacted.

**Data engineers:** With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization.

**Citizen data scientists:** Especially with the rise of more user-friendly features, citizen data scientists who are not professionally trained but have developed data skills are increasingly turning to MLOps to bring AI into their organization.

**Professional data scientists:** Expert data scientists take advantage of these platforms to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment, speeding up data exploration and preparation, as well as model development and training.

**Business stakeholders:** Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations.

### What are the Alternatives to MLOps Platforms?

Alternatives to MLOps Platforms can replace this type of software, either partially or completely:

[Data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) **:** Depending on the use case, businesses might consider data science and machine learning platforms. This software provides a platform for the full end-to-end development of machine learning models and can provide more robust features around operationalizing these algorithms.

[Machine learning software](https://www.g2.com/categories/machine-learning) **:** MLOps Platforms are great for the full-scale monitoring and managing of models, whether that be for computer vision, natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less setup time and development costs.

Many different types of machine learning algorithms perform various tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations looking for point solutions.

#### Software Related to MLOps Platforms

Related solutions that can be used together with MLOps Platforms include:

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although MLOps Platforms offer data preparation features, businesses might opt for a dedicated preparation tool.

[Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications, allowing business intelligence and analytics tools to pull all company data from a single repository. 

[Data labeling software](https://www.g2.com/categories/data-labeling) **:** To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms.

[Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** NLP allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide voice recognition and natural language generation (NLG), which converts data into understandable human language. Some examples of NLP uses include chatbots, translation applications, and social media monitoring tools that scan social media networks for mentions.

### Challenges with MLOps Platforms

Software solutions can come with their own set of challenges. 

**Data requirements:** For most AI algorithms, a great deal of data is required to make it learn the needful. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application.

**Skill shortage:** There is also a shortage of people who understand how to build these algorithms and train them to perform the actions they need. The common user cannot simply fire up AI software and have it solve all their problems.

**Algorithmic bias:** Although the technology is efficient, it is not always effective and is marred with various types of biases in the training data, such as race or gender biases. For example, since many facial recognition algorithms are trained on datasets with primarily white male faces, others are more likely to be falsely identified by the systems.

### Which Companies Should Buy MLOps Platforms?

The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples:

**Financial services:** The use of AI in financial services is prolific, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With MLOps Plat, data science teams can build models with company data and deploy them to both internal and external applications.

**Healthcare:** Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more.

**Retail:** In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers.

### How to Buy MLOps Platforms

#### Requirements Gathering (RFI/RFP) for MLOps Platforms

If a company is just starting out and looking to purchase their first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option.

The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format as well as volume. If the company has amassed a lot of data, they must look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data science platform.

#### Compare MLOps Platforms

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.

#### Selection of MLOps Platforms

**Choose a selection team**

Before getting started, creating a winning team that will work together throughout the entire process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.

**Negotiation**

Just because something is written on a company’s pricing page does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### What Do MLOps Platforms Cost?

As mentioned above, MLOps Platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure. 

As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will often not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.

Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.

#### Return on Investment (ROI)

Businesses decide to deploy MLOps Platforms to derive some degree of ROI. As they are looking to recoup the losses from the software, it is critical to understand its costs. As mentioned above, these platforms are typically billed per user, sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.

### Implementation of MLOps Platforms

**How are MLOps Platforms Implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

**Who is Responsible for MLOps Platforms Implementation?**

It may require a lot of people, or many teams, to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a complete understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of data science, starting with proper data preparation and management.

**What Does the Implementation Process Look Like for MLOps Platforms?**

In terms of implementation, it is typical for the platform deployment to begin in a limited fashion and subsequently roll out in a broader fashion. For example, a retail brand might decide to A/B test their use of a personalization algorithm for a limited number of visitors to their site to better understand how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business).

If the deployment was not successful, the team could go back to the drawing board, attempting to figure out what went wrong. This will involve examining the training data, as well as the algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in predictions), the business might need to go back to basics and review their data as a whole.

**When Should You Implement MLOps Platforms?**

As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must prioritize getting their data in order, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output. 

### MLOps Platforms Trends

**AutoML**

AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more.

**Embedded AI**

Machine and deep learning functionality are getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. Using embedded AI inside software like CRM, marketing automation, and analytics solutions allows users to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and may do so in the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected.

**Machine Learning as a service (MLaaS)**

The software environment has moved to a more granular, microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies offer MLaaS to other businesses.

Developers easily take advantage of these prebuilt algorithms and solutions by feeding them their own data to gain insights. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as businesses continue to rely on these microservices and as the need for AI increases.

**Explainability**

When it comes to machine learning algorithms, especially deep learning, it may be particularly difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency, becomes crucial. MLOps Platforms are increasingly including tools for explainability, helping users build explainability into their models and meet data explainability requirements in legislation such as the European Union's privacy law, the GDPR.