# Best Low-Code Machine Learning Platforms Software

## How Many Low-Code Machine Learning Platforms Software Products Does G2 Track?

**Total Products under this Category:** 21

### Category Stats (Jul 2026)

- **Average Rating:** 4.39/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** KNIME (+0.43%) - Among all products in this category, KNIME recorded the largest rating increase compared to last month

_Last updated: July 27, 2026_

## How Does G2 Rank Low-Code Machine Learning Platforms Software Products?

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

- 30 Analysts and Data Experts
- 3,700+ Authentic Reviews
- 21+ 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 Low-Code Machine Learning Platforms Software
 ![G2 Grid® for Low-Code Machine Learning Platforms Software plotting products by satisfaction and market presence](https://www.g2.com/categories/low-code-machine-learning-platforms/grids.png?focus%5B%5D=1327283&focus%5B%5D=7150&focus%5B%5D=989&focus%5B%5D=21469&focus%5B%5D=16291&focus%5B%5D=38300&focus%5B%5D=16295)

Highlighted products: SAS Viya, Dataiku, Alteryx, Gemini Enterprise Agent Platform, KNIME, Qlik Predict, and Altair AI Studio.

Underlying data: [Grid® JSON](https://www.g2.com/categories/low-code-machine-learning-platforms/grids.json?focus%5B%5D=sas-sas-viya&focus%5B%5D=dataiku&focus%5B%5D=alteryx&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=knime-analytics-platform&focus%5B%5D=qlik-predict&focus%5B%5D=rapidminer-studio)

**Sponsored**

### KNIME

KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1011941&secure%5Bchosen_at%5D=2026-07-27T22%3A39%3A36Z&secure%5Bdisplayable_resource_id%5D=1011941&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1011941&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=16291&secure%5Bresource_id%5D=1011941&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Flow-code-machine-learning-platforms&secure%5Btoken%5D=8892db8e94fef517ae3acbd00e768bd5124d88954d9c47769d88cc107b782461&secure%5Burl%5D=https%3A%2F%2Fwww.knime.com%2Flp%2Fdemo%3Futm_medium%3D3rd-party%26utm_source%3DG2%26utm_campaign%3Dbrand%26utm_term%3Dpaid%26utm_content%3D&secure%5Burl_type%5D=custom_url)

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

SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

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

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Company Website:** https://www.sas.com/
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,863 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Student, Biostatistician
- **Top Industries:** Pharmaceuticals, Banking
- **Company Size:** 33% Large, 33% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of SAS Viya, which simplifies data visualization and enhances decision-making efficiency.
- Users value the **sophisticated analytical capabilities** of SAS Viya, enabling easy deployment and real-time decision-making.
- Users appreciate the **advanced analytical methods** offered by SAS Viya, enhancing decision-making and logistical data analysis capabilities.
- Users value the **end-to-end data lifecycle tooling** of SAS Viya, enhancing business insight and strategic decision-making.
- Users love the **intuitive interface** of SAS Viya, making data analysis and model deployment effortless for all skill levels.

##### Cons

- Users find SAS Viya to have a **learning difficulty** , making it challenging for non-technical individuals to navigate effectively.
- Users find the **learning curve steep** , making it challenging for non-technical users to navigate SAS Viya effectively.
- Users find the **visualization complexity** in SAS Viya challenging, particularly for non-technical users and beginners.
- Users struggle with the **difficult learning curve** of SAS Viya, particularly for new and non-technical users.
- Users find the **expensive pricing** of SAS Viya to be a significant barrier to entry for potential adoption.

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

**["Effective Data Analysis with SAS Viya"](https://www.g2.com/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

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

**["SAS Viya: Powerful AI & Data Analysis with Seamless Integrations"](https://www.g2.com/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Verified User in Hospital & Health Care_

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

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

Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### Who Is the Company Behind Dataiku?

- **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku)
- **Company Website:** https://Dataiku.com
- **Year Founded:** 2013
- **HQ Location:** New York, NY
- **Twitter:** @dataiku (22,917 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,619 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Scientist, Data Analyst
- **Top Industries:** Financial Services, Pharmaceuticals
- **Company Size:** 60% Large, 22% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate how Dataiku facilitates **easy ML development** , allowing focus on building models without the complexity.
- Users love the **ease of use** in Dataiku, simplifying complex tasks and enhancing their data analysis experience.
- Users appreciate the **ease of usability** in Dataiku, enabling collaboration for both technical and non-technical users.
- Users appreciate the **easy integrations** of Dataiku, facilitating smooth collaboration and deployment across various analytics tools.
- Users benefit from the **productivity improvement** of Dataiku, enabling faster project development and enhanced career growth.

##### Cons

- Users find the **steep learning curve** of Dataiku challenging, making it tough for beginners to master the platform.
- Users find the **steep learning curve** challenging for beginners, impacting their ability to effectively use Dataiku.
- Users find the **difficult learning** curve challenging, particularly for beginners navigating advanced features.
- Users experience **slow performance** with Dataiku when handling large datasets, affecting efficiency and productivity.
- Users find Dataiku **expensive** , especially for smaller organizations and projects, impacting accessibility and affordability.

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

**["Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"](https://www.g2.com/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

**["Build Faster Workflows with Connected Data from many providers or distinct data sources"](https://www.g2.com/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

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

### [Alteryx](https://www.g2.com/products/alteryx/reviews)

Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial.

**Average Rating:** 4.6/5.0

**Total Reviews:** 851

#### Who Is the Company Behind Alteryx?

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** https://www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,149 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,304 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Analyst, Analyst
- **Top Industries:** Financial Services, Accounting
- **Company Size:** 63% Large, 21% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for non-technical users.
- Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data preparation and analysis.
- Users love the **intuitive design** of Alteryx, making data management and workflow creation effortless and efficient.
- Users find Alteryx to be **very easy to learn and use** , enhancing their data workflow and automation experience.
- Users appreciate the **efficiency** of Alteryx, enabling quick data processing and streamlined workflows without complex coding.

##### Cons

- Users mention that Alteryx has a **high cost** which can be challenging for small teams and startups.
- Users find a **steep learning curve** for advanced features, making it challenging for beginners to master Alteryx quickly.
- Users point out the **missing features** in Alteryx, such as limited connectors and issues with output flexibility.
- Users find **learning difficulty** in Alteryx due to confusing tools and troubleshooting errors, especially for beginners.
- Users encounter **slow performance** when processing large datasets, impacting efficiency and usability in Alteryx.

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

**["Powerful, Time-Saving Data Prep But Pricey, Windows-Only, and Weak on Reporting"](https://www.g2.com/survey_responses/alteryx-review-12999843)**

**Rating:** 4.5/5.0 stars

_— jayesh l._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12999843)

**["Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights"](https://www.g2.com/survey_responses/alteryx-review-12983224)**

**Rating:** 4.5/5.0 stars

_— Akhil S._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12983224)

### [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

#### 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:** https://www.linkedin.com/company/1441/ (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)

## FAQs About Low-Code Machine Learning Platforms Software

Generated using AI

Last updated: June 3, 2026

### Which low code ml platform gives the best balance of price and automation features

Based on G2 reviews, these products are the most consistently mentioned for low-code automation and model-building workflows.

- [Alteryx](https://www.g2.com/products/alteryx) — automated data prep and reporting.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya) — automated ML pipelines and dashboards.
- [Dataiku](https://www.g2.com/products/dataiku) — visual workflows with coding flexibility.
- [Altair AI Studio](https://www.g2.com/products/rapidminer-studio) — AutoML with visual workflow design.

### Low code machine learning platforms that integrate well with existing web apps and APIs

According to verified users, integration strength shows up in a few recurring patterns: API access, compatibility with existing cloud or business systems, and the ability to move data between tools without heavy custom work. Recent reviewers highlight platforms that connect to multiple data sources, support open languages or APIs, and fit into broader enterprise workflows. They also note that integration quality can vary by connector and environment, especially when teams need external systems, custom pipelines, or cross-cloud support. Buyers evaluating this area should look closely at how easily a platform handles ingestion, deployment, and workflow handoffs using the systems their team already depends on.

### What���s the easiest low code ml platform for a non data scientist to build models

According to verified users, ease for non-data scientists usually comes from drag-and-drop design, guided workflows, and the option to build models without writing code. Recent G2 reviews frequently mention beginner-friendly interfaces, visual pipelines, AutoML support, and smoother onboarding when platforms balance simplicity with room to grow. Reviewers also point out that many products are approachable at first but still have learning curves once projects become more complex or datasets get larger. For buyers, the most practical signal is whether non-technical users can prepare data, test models, and share outputs without relying on specialists for every step of the workflow.

### What are the best low code machine learning platforms

Based on G2 reviews, these products appear most often in recent feedback for low-code machine learning use cases.

- [Alteryx](https://www.g2.com/products/alteryx) — drag-and-drop data prep and automation.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya) — visual modeling and governed analytics.
- [Dataiku](https://www.g2.com/products/dataiku) — collaborative end-to-end ML workflows.
- [Altair AI Studio](https://www.g2.com/products/rapidminer-studio) — no-code modeling for engineering data.

### What features define modern low-code machine learning platforms

According to verified users, modern low-code machine learning platforms are defined by visual workflow building, automated model selection, data preparation tools, and the flexibility to mix no-code steps with code when needed. Recent reviews also repeatedly mention dashboarding, reporting, deployment support, model monitoring, collaboration, and integration with common data sources or APIs. Buyers should also weigh practical usability signals that come up often in reviews, such as onboarding experience, interface clarity, workflow speed, and how well the platform supports both technical and non-technical contributors. The strongest products tend to reduce manual work while keeping model building, data movement, and operational handoffs in one environment.

### [KNIME](https://www.g2.com/products/knime-analytics-platform/reviews)

KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice.

**Average Rating:** 4.5/5.0

**Total Reviews:** 102

#### Who Is the Company Behind KNIME?

- **Seller:** [KNIME](https://www.g2.com/sellers/knime)
- **Company Website:** https://knime.com
- **Year Founded:** 2008
- **HQ Location:** Zurich, Switzerland
- **Twitter:** @knime (7,998 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/692207?trk=tyah&trkInfo=clickedVertical%3Acompany%2CclickedEntityId%3A692207%2Cidx%3A2-1-4%2CtarId%3A1454002156993%2Ctas%3Aknime (244 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Higher Education
- **Company Size:** 42% Large, 33% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find KNIME's **ease of use** exceptional, enabling non-technical individuals to create workflows effortlessly.
- Users value the **coding ease** of KNIME, allowing even non-coders to build complex workflows effortlessly.
- Users find KNIME's **ease of learning** beneficial, allowing beginners to start delivering results quickly.
- Users find KNIME to be a **powerful and easy-to-learn platform** , enabling effective data analysis and AI solution development.
- Users highlight the **effortless data visualization** capabilities of KNIME, making complex data accessible and easy to understand.

##### Cons

- Users find the **initial learning curve challenging** , especially for those new to data science and visual programming.
- Users experience significant **memory usage issues** with KNIME, leading to slow performance, especially with large files.
- Users report **storage limitations** with KNIME, facing memory availability issues that hinder performance with large files.
- Users note that **data management issues** persist in KNIME, particularly with file handling and database compatibility.
- Users feel the **lack of learning resources** hinders their ability to fully utilize KNIME's capabilities.

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

**["KNIME’s Free No-Code Drag-and-Drop Analytics, from Descriptive to Agentic AI"](https://www.g2.com/survey_responses/knime-review-12992618)**

**Rating:** 5.0/5.0 stars

_— Guylaine B._

[Read full review](https://www.g2.com/survey_responses/knime-review-12992618)

**["KNIME’s Visual Workflows - One of the best tool for Auditing, Accounting & Finance Professionals"](https://www.g2.com/survey_responses/knime-review-12976842)**

**Rating:** 5.0/5.0 stars

_— Charm M._

[Read full review](https://www.g2.com/survey_responses/knime-review-12976842)

### [Qlik Predict](https://www.g2.com/products/qlik-predict/reviews)

Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple user experience focused on augmenting their intuition through machine intelligence. With AutoML, you can easily generate machine learning models, make predictions, and plan decisions – all within an intuitive, code-free user interface. Machine learning (ML) is a branch of artificial intelligence (AI) focused on the process of recognizing patterns in historical data to predict outcomes in the future. ML uses historically observed data as an input, applies a mathematical process against that data, and creates an output called a machine learning model based on patterns in historical data. This model can then be used to make future predictions and test scenarios.

**Average Rating:** 4.4/5.0

**Total Reviews:** 78

#### Who Is the Company Behind Qlik Predict?

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,130 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,551 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

#### Who Uses This Product?

- **Who Uses This:** Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Large, 31% Small

#### What Do G2 Reviewers Say About Qlik Predict?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **automation features** of Qlik Predict, enabling quick and easy model building without extensive technical skills.
- Users appreciate the **ease of use** of Qlik Predict, experiencing an intuitive no-code interface for hassle-free implementation.
- Users value the **seamless AI integration** of Qlik Predict, enhancing efficiency in building and deploying predictive models.
- Users appreciate the **intuitive no-code interface** of Qlik Predict, enabling quick model creation without technical expertise.
- Users praise the **user-friendly AI capabilities** of Qlik Predict, enabling easy implementation of predictive models without coding.

##### Cons

- Users find the **limited customization** of Qlik Predict restricts advanced functionalities and deployment flexibility.
- Users face **deployment issues** with Qlik Predict, as options are limited and lack flexibility for external integrations.
- Users note the **limited features** of Qlik Predict, especially in customization and deployment flexibility for advanced needs.
- Users must have **required knowledge of data science** to effectively optimize models and interpret results with Qlik Predict.
- Users find the **tool limitations** of Qlik Predict restrict customization and flexibility for advanced model development.

#### What Are Recent G2 Reviews of Qlik Predict?

**["Qlik AutoML"](https://www.g2.com/survey_responses/qlik-predict-review-11001365)**

**Rating:** 4.0/5.0 stars

_— Anju P._

[Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11001365)

**["Describe your experience in one short sentence."](https://www.g2.com/survey_responses/qlik-predict-review-11007278)**

**Rating:** 5.0/5.0 stars

_— Cristian C._

[Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11007278)

### [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews)

Altair AI Studio (formerly RapidMiner Studio) is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Altair AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Altair AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Altair AI studio enables organizations to derive significant value from AI with minimal cost and operational impact.

**Average Rating:** 4.6/5.0

**Total Reviews:** 494

#### Who Is the Company Behind Altair AI Studio?

- **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7)
- **Company Website:** https://www.altair.com/
- **Year Founded:** 1985
- **HQ Location:** Troy, MI
- **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (2,774 employees on LinkedIn®)
- **Ownership:** NASDAQ:ALTR

#### Who Uses This Product?

- **Who Uses This:** Student, Data Scientist
- **Top Industries:** Higher Education, Education Management
- **Company Size:** 42% Small, 30% Medium

#### What Do G2 Reviewers Say About Altair AI Studio?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Altair AI Studio, benefiting from its intuitive drag-and-drop interface.
- Users value the **ease of using machine learning without coding** , making it accessible and beneficial for education and organizations.
- Users value the **seamless AI integration** of Altair AI Studio, enhancing decision making and efficiency in data analysis.
- Users value the **advanced machine learning and analytics** in Altair AI Studio, enhancing decision-making and efficiency.
- Users value the **automation features** of Altair AI Studio, streamlining processes and enhancing data analysis efficiency.

##### Cons

- Users find the **complexity** of Altair AI Studio challenging, especially with integration and performance issues.
- Users often face **slow performance with large datasets** , which can impact their overall experience with Altair AI Studio.
- Users report experiencing **slow performance** with Altair AI Studio, especially when working with large or complex datasets.
- Users find the **complexity issues** of Altair AI Studio frustrating, especially due to limited support in Japanese.
- Users find the **complex usage** of Altair AI Studio challenging due to steep learning curves and limited documentation.

#### What Are Recent G2 Reviews of Altair AI Studio?

**["Essential Tool for Streamlined Sensor Analysis"](https://www.g2.com/survey_responses/altair-ai-studio-review-12568188)**

**Rating:** 5.0/5.0 stars

_— Ayçe M._

[Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12568188)

**["Great tool for easy data analysis and testing of AI models"](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)**

**Rating:** 4.5/5.0 stars

_— Sanjeet S._

[Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)

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

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data.

**Average Rating:** 4.4/5.0

**Total Reviews:** 134

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

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** https://www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity (74,660 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Small, 32% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in IBM watsonx.ai, facilitating quicker AI integration and effective management.
- Users appreciate the **model variety** of IBM watsonx.ai, enabling customized training on existing models for enhanced performance.
- Users appreciate the **seamless integration of enterprise-grade AI** in IBM watsonx.ai, enhancing decision-making and workflow efficiency.
- Users appreciate the **enterprise-grade integrated studio** of IBM watsonx.ai for seamless AI training and reliable insights.
- Users value the **enterprise-grade AI integration** of IBM watsonx.ai, enhancing decision-making and business operations efficiently.

##### Cons

- Users find the **difficult learning** curve of IBM watsonx.ai daunting, making it less accessible for newcomers and smaller teams.
- Users find the **complex setup** of IBM watsonx.ai challenging, making it less suitable for small teams and beginners.
- Users find the **steep learning curve** of IBM watsonx.ai challenging, making it less accessible for non-technical teams.
- Users find the product **expensive** and challenging for small teams, citing high costs and complex setup requirements.
- Users find the **complex setup** of IBM watsonx.ai challenging, especially for beginners and small teams.

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

**["Enterprise-Ready Prompt Lab for Comparing Models and Building Project-Based AI Solutions"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13088968)**

**Rating:** 4.5/5.0 stars

_— Aleksander M._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13088968)

**["Enterprise-Ready AI with Strong Governance and Flexible Model Support"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)

### [Clarifai](https://www.g2.com/products/clarifai/reviews)

Clarifai is a leader in AI orchestration and development, helping organizations, teams, and developers build, deploy, orchestrate, and operationalize AI at scale. Clarifai’s cutting-edge AI workflow orchestration platform leverages today's modern AI technologies like Large Language Models (LLMs), Large Vision Models (LVMs), and Retrieval Augmented Generation (RAG), data labeling, inference, and more, and is available in cloud, on-premises, or hybrid environments. Founded in 2013, Clarifai has been used to build more than 1.5 million AI models with more than 400,000 users in 170 countries. Learn more at www.clarifai.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 66

#### Who Is the Company Behind Clarifai?

- **Seller:** [Clarifai](https://www.g2.com/sellers/clarifai)
- **Year Founded:** 2013
- **HQ Location:** Wilmington, Delaware
- **Twitter:** @clarifai (10,922 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10064814/ (51 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 62% Small, 27% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use and powerful features** of Clarifai, enhancing their AI image and text projects.
- Users value the **diverse model variety** in Clarifai, enabling tailored solutions for various application scenarios efficiently.
- Users highlight the **accuracy and efficiency of Clarifai's AI technology** , making it a great choice for diverse projects.
- Users find Clarifai's **intuitive UI** easy to navigate, enhancing their experience with powerful pre-trained models.
- Users commend Clarifai for its **easy AI integration** , enabling fast and accurate tagging for various application scenarios.

##### Cons

- Users find the **cost prohibitively expensive** for small projects, hindering accessibility for individual developers and non-profits.
- Users find the **complexity** of Clarifai challenging, especially regarding documentation and advanced feature understanding.
- Users find the **learning curve steep** for new users, making initial navigation and understanding challenging.
- Users express concern over the **lack of resources** , particularly for small developers, impacting accessibility and usability.
- Users find the **poor documentation** of Clarifai frustrating, often requiring external help for guidance and clarity.

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

**["Clean UI, Powerful AI Platform with Reliable Performance and Responsive Support"](https://www.g2.com/survey_responses/clarifai-review-13072233)**

**Rating:** 5.0/5.0 stars

_— Ross M._

[Read full review](https://www.g2.com/survey_responses/clarifai-review-13072233)

**["Helped with my projects! Would recommend!"](https://www.g2.com/survey_responses/clarifai-review-11387093)**

**Rating:** 4.0/5.0 stars

_— Verified User in Information Technology and Services_

[Read full review](https://www.g2.com/survey_responses/clarifai-review-11387093)

### [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:** 53

#### 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:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 34% Medium, 32% 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?

**["A powerhouse for end-to-end ML, but be prepared for a steep learning curve"](https://www.g2.com/survey_responses/amazon-sagemaker-review-12959870)**

**Rating:** 5.0/5.0 stars

_— Lokesh S._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12959870)

**["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)

### [DataRobot](https://www.g2.com/products/datarobot/reviews)

DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.

**Average Rating:** 4.4/5.0

**Total Reviews:** 28

#### Who Is the Company Behind DataRobot?

- **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot)
- **Year Founded:** 2012
- **HQ Location:** Boston, Massachusetts
- **Twitter:** @DataRobot (19,225 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2672915/ (883 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 52% Small, 31% Medium

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

**["Fast, Insightful Automated Modeling with DataRobot"](https://www.g2.com/survey_responses/datarobot-review-11788506)**

**Rating:** 4.5/5.0 stars

_— Verified User in Financial Services_

[Read full review](https://www.g2.com/survey_responses/datarobot-review-11788506)

**["Streamlined AutoML with Quick Deployment"](https://www.g2.com/survey_responses/datarobot-review-13119818)**

**Rating:** 4.5/5.0 stars

_— Verified User_

[Read full review](https://www.g2.com/survey_responses/datarobot-review-13119818)

### [Google Cloud AutoML](https://www.g2.com/products/google-cloud-automl/reviews)

Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited expertise to train high-quality custom models tailored to their specific business needs. By leveraging Google's advanced transfer learning and neural architecture search technologies, AutoML simplifies the process of building, deploying, and scaling machine learning models, making AI more accessible to a broader audience. Key Features and Functionality: - Automated Model Training: AutoML automates the selection of model architecture and hyperparameter tuning, reducing the need for manual intervention and specialized knowledge. - User-Friendly Interface: The platform offers an intuitive graphical interface that allows users to upload data, train models, and manage deployments with ease. - Versatile Model Types: AutoML supports various data types and tasks through specialized services: - AutoML Vision: For image classification and object detection. - AutoML Natural Language: For text classification, sentiment analysis, and entity recognition. - AutoML Translation: For creating custom translation models between language pairs. - AutoML Video Intelligence: For video classification and object tracking. - AutoML Tables: For structured data tasks like regression and classification. - Seamless Integration: AutoML integrates with other Google Cloud services, facilitating efficient data management, model deployment, and scalability. Primary Value and Problem Solving: Google Cloud AutoML democratizes machine learning by enabling users without deep technical expertise to develop and deploy custom models. This accessibility allows businesses to harness the power of AI to solve complex problems, such as improving customer experiences through personalized recommendations, automating content moderation, enhancing language translation services, and gaining insights from large datasets. By reducing the barriers to entry, AutoML empowers organizations to innovate and stay competitive in their respective industries.

**Average Rating:** 4.1/5.0

**Total Reviews:** 25

#### Who Is the Company Behind Google Cloud AutoML?

- **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:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 38% Small, 35% Medium

#### What Do G2 Reviewers Say About Google Cloud AutoML?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **seamless AI integration** of Google Cloud AutoML, facilitating high-quality ML model training effortlessly.
- Users appreciate the **ease of use** of Google Cloud AutoML, finding it intuitive and well-documented for training models.
- Users value the **easy integrations** of Google Cloud AutoML, enhancing their experience with seamless connections to other Google services.
- Users appreciate the **seamless integration** of Google Cloud AutoML with other Google services, enhancing their machine learning experience.
- Users find the **intuitive interface** of Google Cloud AutoML simplifies the process of training high-quality machine learning models.

##### Cons

- Users find the **pricing expensive** for small projects or students, making it less accessible for some users.
- Users find the **pricing expensive** , making it challenging for small projects or students to utilize Google Cloud AutoML.

#### What Are Recent G2 Reviews of Google Cloud AutoML?

**["Faster ML Model Development by Google"](https://www.g2.com/survey_responses/google-cloud-automl-review-13088393)**

**Rating:** 4.0/5.0 stars

_— Verified User in Information Technology and Services_

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13088393)

**["Easy to Use Yet Customizable, with Room to Grow into Vertex AI"](https://www.g2.com/survey_responses/google-cloud-automl-review-13076121)**

**Rating:** 4.5/5.0 stars

_— Nikhil M._

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13076121)

### [Pecan](https://www.g2.com/products/pecan/reviews)

Pecan AI is a predictive analytics platform that helps business teams understand what’s likely to happen next, while there is still time to act. With Pecan’s Predictive AI Agent, teams can turn business questions into reliable predictions for use cases like customer churn, demand forecasting, and lifetime value, without relying on long, complex data science projects. The platform automatically handles data preparation, feature engineering, modeling, validation, and delivery, and provides transparent, explainable predictions that integrate into tools like Salesforce, HubSpot, Snowflake, and BI systems to drive real business outcomes.

**Average Rating:** 4.7/5.0

**Total Reviews:** 36

#### Who Is the Company Behind Pecan?

- **Seller:** [Pecan.ai](https://www.g2.com/sellers/pecan-ai)
- **Company Website:** https://www.pecan.ai
- **Year Founded:** 2018
- **HQ Location:** US, Israel
- **Twitter:** @pecan\_ai (1,135 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/pecan-ai/ (89 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Retail
- **Company Size:** 54% Medium, 21% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of Pecan, allowing simple model building without requiring deep technical skills.
- Users praise Pecan's **excellent customer support** , emphasizing prompt assistance and valuable guidance throughout their learning process.
- Users highlight the **speed of development** with Pecan, reducing model creation from months to weeks efficiently.
- Users value Pecan's **exceptional problem-solving support** , enhancing their ability to leverage data for actionable insights.
- Users highlight the **implementation ease** of Pecan, facilitating swift model deployment and enhancing productivity significantly.

##### Cons

- Users experience a **steep learning curve** with Pecan, requiring at least intermediate SQL knowledge to navigate effectively.
- Users desire **deeper control over model selection and custom optimization metrics** , finding the auto-selection process limiting.
- Users feel the **limited model customization** restricts their ability to tailor solutions for specific use cases effectively.
- Users experience a **steep learning curve** initially, particularly with data structure and SQL understanding required.
- Users express a desire for **limited customization** , wishing for more control over model selection and optimization metrics.

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

**["AI Chatbox Integration Makes Feature Development Easy to Explore and Iterate"](https://www.g2.com/survey_responses/pecan-review-12878894)**

**Rating:** 4.0/5.0 stars

_— Yuqi L._

[Read full review](https://www.g2.com/survey_responses/pecan-review-12878894)

**["Intuitive Platform with Exceptional Support"](https://www.g2.com/survey_responses/pecan-review-12654479)**

**Rating:** 5.0/5.0 stars

_— J G._

[Read full review](https://www.g2.com/survey_responses/pecan-review-12654479)

### [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

#### 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:** https://www.linkedin.com/company/microsoft/ (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?

**["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)

**["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)

### [Neuton AutoML](https://www.g2.com/products/neuton-automl/reviews)

Neuton (https://neuton.ai), a new AutoML solution, allows users to build compact AI models with just a few clicks and without any coding. Neuton also happens to be the most EXPLAINABLE Neural Network Framework and AutoML solution currently available on the market. It allows users to evaluate the model quality from various perspectives and interpret prediction results. Neuton Explainability Office: - Exploratory Data Analysis - Feature Importance Matrix with class granularity - Model Interpreter - Feature Influence Matrix - Validate Model on New Data - Model-to-Data Relevance Indicators historical and for every prediction - Model Quality Index - Confidence Interval - Extensive list of supported metrics with Radar Diagram

**Average Rating:** 4.5/5.0

**Total Reviews:** 17

#### Who Is the Company Behind Neuton AutoML?

- **Seller:** [Bell Integrator](https://www.g2.com/sellers/bell-integrator)
- **Year Founded:** 2003
- **HQ Location:** San Jose, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/bellintegrator/ (703 employees on LinkedIn®)

#### Who Uses This Product?

- **Company Size:** 35% Small, 35% Large

#### What Are Recent G2 Reviews of Neuton AutoML?

**["A Comprehensive and Efficient Solution for Automating Machine Learning Model Development"](https://www.g2.com/survey_responses/neuton-automl-review-7623010)**

**Rating:** 4.5/5.0 stars

_— Rajesh S._

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-7623010)

**["Cloud based ML platform for everyone."](https://www.g2.com/survey_responses/neuton-automl-review-8043519)**

**Rating:** 4.5/5.0 stars

_— Abhuday T._

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-8043519)

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[Browse Low-Code Machine Learning Platforms Themes](/categories/low-code-machine-learning-platforms/themes)

 ![Adam Crivello](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Adam Crivello")
AC

Researched and written by [Adam Crivello](https://research.g2.com/insights/author/adam-crivello)

Updated April 9, 2026

Low-code machine learning (ML) platforms enable businesses to build, train, and deploy ML models primarily through visual or guided interfaces, using drag-and-drop tools, AutoML workflows, and wizard-style guidance to make predictive modeling and AI development accessible to business analysts, subject matter experts, and data scientists without extensive coding expertise.

### Core Capabilities of Low-Code Machine Learning Platforms

To qualify for inclusion in the Low-Code Machine Learning (ML) Platforms category, a product must:

- Provide a graphical, low-code or no-code interface to build and train custom ML models on user-provided data
- Include built-in functionality to evaluate trained models
- Offer direct deployment options from the interface, such as batch scoring, API endpoints, or managed service environments
- Support data ingestion through uploads or connectors to databases, cloud storage, or other sources
- Enable collaboration and governance through features like role-based access, project or workspace management, or auditability

### Common Use Cases for Low-Code Machine Learning Platforms

Business analysts, data scientists, and non-technical teams use low-code ML platforms to accelerate AI adoption without deep programming expertise. Common use cases include:

- Building and deploying predictive models for use cases such as churn prediction, demand forecasting, and fraud detection
- Empowering non-technical subject matter experts to contribute to ML model development using visual interfaces
- Standardizing the deployment and governance of ML models into production environments across the enterprise

### How Low-Code Machine Learning Platforms Differ from Other Tools

Unlike traditional [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which require extensive programming and are primarily designed for experienced data scientists, low-code ML platforms deliver end-to-end ML lifecycle functionality through a user-friendly interface. Some enterprise cloud providers offer low-code ML capabilities within broader AI ecosystems, while dedicated vendors focus solely on visual model development and deployment.

### Insights from G2 on Low-Code Machine Learning Platforms

Based on category trends on G2, the visual model builder and AutoML capabilities stand out as standout features. These platforms deliver faster time-to-model deployment and reduced dependency on data science resources as primary benefits of adoption.

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