# Best Data Science and Machine Learning Platforms - Page 27 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Data science and machine learning (DSML) platforms provide tools to build, deploy, and monitor machine learning (ML) algorithms by combining data with intelligent, decision-making models to support business solutions. These platforms may offer prebuilt algorithms and visual workflows for nontechnical users or require more advanced development skills for complex model creation. Core capabilities of data science and machine learning (DSML) software To qualify for inclusion in the Data Science and Machine Learning (DSML) Platforms category, a product must: - Present a way for developers to connect data to algorithms so they can learn and adapt - Allow users to create ML algorithms and offer prebuilt algorithms for novice users - Provide a platform for deploying AI at scale How DSML software differs from other tools DSML platforms differ from traditional platform-as-a-service (PaaS) offerings by providing ML–specific functionality, such as prebuilt algorithms, model training workflows, and automated features that reduce the need for extensive data science expertise. Insights from G2 Reviews on DSML software According to G2 review data, users highlight the value of streamlined model development, ease of deployment, and options that support both nontechnical and advanced practitioners through visual interfaces or coding-based workflows. ## Top Data Science and Machine Learning Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,283 reviews) | Unified lakehouse ML and analytics workflows | "[Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines](https://www.g2.com/survey_responses/databricks-review-12946286)" | | 2 | [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) | 4.3/5.0 (652 reviews) | End-to-end ML lifecycle with GCP-native MLOps | "[Seamless Google Suite Integration for Everyday Work](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)" | | 3 | [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) | 4.3/5.0 (757 reviews) | End-to-end ML lifecycle with governed model deployment | "[SAS Viya is a Powerful Analytics](https://www.g2.com/survey_responses/sas-viya-review-11702846)" | | 4 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (707 reviews) | SQL-native ML pipelines with unified data warehousing | "[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)" | | 5 | [Deepnote](https://www.g2.com/products/deepnote/reviews) | 4.5/5.0 (377 reviews) | Collaborative notebook analytics with multi-source integration | "[Clarity for complex nutrition work](https://www.g2.com/survey_responses/deepnote-review-12699174)" | | 6 | [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) | 4.4/5.0 (159 reviews) | Unified lakehouse analytics for hybrid AI workloads | "[Unified Data Management with Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)" | | 7 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (201 reviews) | End-to-end ML workflows with no-code/code flexibility | "[Streamlined Data Management with Stellar Support](https://www.g2.com/survey_responses/dataiku-review-12983575)" | | 8 | [MATLAB](https://www.g2.com/products/matlab/reviews) | 4.5/5.0 (750 reviews) | Numerical simulation and ML algorithm prototyping | "[Fast Engineering Data Analysis with Powerful Visualization and Toolboxes](https://www.g2.com/survey_responses/matlab-review-12904428)" | | 9 | [Hex](https://www.g2.com/products/hex-tech-hex/reviews) | 4.5/5.0 (397 reviews) | Polyglot SQL-Python notebooks with AI-assisted analysis | "[Effortless Data Analysis with Powerful AI](https://www.g2.com/survey_responses/hex-review-12262172)" | | 10 | [Anaconda Core](https://www.g2.com/products/anaconda-core/reviews) | 4.5/5.0 (235 reviews) | Dependency-free Python environment setup for data science | "[All-in-One Toolkit for Data Science Workflows](https://www.g2.com/survey_responses/anaconda-core-review-12706297)" | ## How Many Data Science and Machine Learning Platforms Products Does G2 Track? **Total Products under this Category:** 892 ### Category Stats (Jun 2026) - **Average Rating**: 4.45/5 The average rating of products in this category, based on all submitted ratings - **New Reviews This Quarter**: 232 - **Buyer Segments**: Mid-Market 38% │ Small-Business 32% │ Enterprise 29% Represents the distribution of reviewers across all products in this category. - **Top Trending Product**: OPUS (+7.14%) - Among all products in this category, OPUS recorded the largest rating increase compared to last month *Last updated: June 01, 2026* ## How Does G2 Rank Data Science and Machine Learning Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 13,800+ Authentic Reviews - 892+ 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. ## Which Data Science and Machine Learning Platforms Is Best for Your Use Case? - **Leader:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [Saturn Cloud](https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews) - **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Top Trending:** [Hex](https://www.g2.com/products/hex-tech-hex/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### Cloudera Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments. [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=692&secure%5Bdisplayable_resource_id%5D=692&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1886&secure%5Bresource_id%5D=692&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-science-and-machine-learning-platforms%2Fenterprise%3Fpage%3D2&secure%5Btoken%5D=ef573ff4f00e0cafdb4828cf2ea63c5b8b665c65ac084c69ea58c0eaddfc1fbe&secure%5Burl%5D=https%3A%2F%2Fwww.cloudera.com%2Fproducts%2Fcloudera-data-platform%2Fcdp-demos.html%3Finternal_link%3Dp18%23get-started&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026? ### 1. [PostEra](https://www.g2.com/products/postera/reviews) PostEra is a biotechnology company leveraging machine learning to revolutionize medicinal chemistry and expedite the discovery of new medicines. Their proprietary AI platform, Proton, addresses the complexities of drug development by enhancing the design and synthesis of small molecules, thereby accelerating the path from concept to clinical trials. Key Features and Functionality: - Proton AI Platform: Utilizes advanced machine learning algorithms to optimize medicinal chemistry processes, improving the efficiency and accuracy of drug discovery. - Collaborative Partnerships: Engages in strategic alliances with leading biopharmaceutical companies, including Amgen and Pfizer, to co-develop innovative therapeutics. - COVID Moonshot Initiative: Spearheaded a global open-science project aimed at rapidly identifying antiviral compounds during the COVID-19 pandemic. Primary Value and User Solutions: PostEra's integration of AI into medicinal chemistry streamlines the drug discovery pipeline, reducing time and costs associated with bringing new treatments to market. By partnering with major pharmaceutical firms and leading open-science initiatives, PostEra enhances the development of effective therapies, ultimately benefiting patients through faster access to innovative medicines. **Who Is the Company Behind PostEra?** - **Seller:** [PostEra](https://www.g2.com/sellers/postera) - **Year Founded:** 2019 - **HQ Location:** Boston, US - **LinkedIn® Page:** https://www.linkedin.com/company/postera-ai (5,229 employees on LinkedIn®) ### 2. [Powerbiailens](https://www.g2.com/products/powerbiailens/reviews) Powerbiailens is an AI-powered extension designed to enhance Microsoft Power BI by integrating advanced visual analytics capabilities. It enables users to create more insightful and interactive data visualizations, facilitating deeper data exploration and understanding. Key Features and Functionality: - Advanced Visual Analytics: Offers a suite of AI-driven tools to generate complex visualizations, uncovering hidden patterns and trends within datasets. - Seamless Integration: Easily integrates with existing Power BI environments, allowing users to enhance their reports without disrupting current workflows. - Interactive Dashboards: Provides dynamic and interactive dashboard elements, enabling users to drill down into data points for more detailed analysis. - Automated Insights: Utilizes machine learning algorithms to automatically generate insights, reducing the time required for manual data interpretation. Primary Value and User Solutions: Powerbiailens addresses the challenge of extracting meaningful insights from complex datasets by enhancing Power BI's visualization capabilities. It empowers users to create more engaging and informative reports, leading to better decision-making and a deeper understanding of their data. **Who Is the Company Behind Powerbiailens?** - **Seller:** [Power BI AI Lens](https://www.g2.com/sellers/power-bi-ai-lens) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 3. [Powpow](https://www.g2.com/products/powpow/reviews) Powpow is an advanced AI-powered platform designed to revolutionize the way businesses manage and analyze their data. By leveraging cutting-edge machine learning algorithms, Powpow enables organizations to extract actionable insights, automate complex processes, and enhance decision-making capabilities. Its intuitive interface ensures that users, regardless of technical expertise, can harness the full potential of their data assets. Key Features and Functionality: - Data Integration: Seamlessly connects with various data sources, ensuring comprehensive data aggregation. - Advanced Analytics: Utilizes sophisticated algorithms to uncover patterns and trends within datasets. - Automated Reporting: Generates real-time reports and visualizations, facilitating informed decision-making. - Customizable Dashboards: Offers personalized dashboards tailored to specific business needs. - Scalability: Adapts to businesses of all sizes, from startups to large enterprises. Primary Value and Solutions Provided: Powpow addresses the challenge of data overload by providing a streamlined platform that simplifies data analysis and interpretation. It empowers users to make data-driven decisions swiftly, reducing the time and resources spent on manual data processing. By automating routine tasks and offering deep insights, Powpow enhances operational efficiency, drives innovation, and fosters a competitive edge in the market. **Who Is the Company Behind Powpow?** - **Seller:** [PowPow](https://www.g2.com/sellers/powpow) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 4. [PrecogX](https://www.g2.com/products/precogx/reviews) PrecogX is an advanced artificial intelligence platform designed to empower businesses with predictive analytics and data-driven decision-making capabilities. By leveraging cutting-edge machine learning algorithms, PrecogX enables organizations to anticipate market trends, optimize operations, and enhance customer experiences. Its intuitive interface and robust analytics tools make it accessible to both technical and non-technical users, facilitating seamless integration into existing workflows. Key Features and Functionality: - Predictive Analytics: Utilizes sophisticated machine learning models to forecast future trends and behaviors, allowing businesses to make proactive decisions. - Data Integration: Aggregates data from multiple sources, providing a comprehensive view of business operations and customer interactions. - Customizable Dashboards: Offers user-friendly dashboards that can be tailored to display key performance indicators and metrics relevant to specific business needs. - Automated Reporting: Generates detailed reports automatically, saving time and ensuring consistency in data analysis. - Scalability: Designed to handle large datasets and scale with the growth of the business, ensuring performance remains optimal as data volume increases. Primary Value and Solutions Provided: PrecogX addresses the challenge of transforming vast amounts of raw data into actionable insights. By providing predictive analytics and real-time data processing, it enables businesses to identify opportunities, mitigate risks, and improve operational efficiency. This leads to informed strategic planning, enhanced customer satisfaction, and a competitive edge in the market. **Who Is the Company Behind PrecogX?** - **Seller:** [PrecogX](https://www.g2.com/sellers/precogx) - **Year Founded:** 2025 - **HQ Location:** Santa Clara, US - **LinkedIn® Page:** https://linkedin.com/company/precogx (1 employees on LinkedIn®) ### 5. [Predict Expert AI](https://www.g2.com/products/predict-expert-ai/reviews) Predict Expert AI is at the forefront of integrating artificial intelligence into business operations, offering bespoke AI models and applications tailored to diverse industry needs. By embedding advanced AI capabilities into existing systems, the company enhances operational efficiency, streamlines processes, and drives profitability. Their solutions are designed to provide real-time insights, transforming businesses into smarter, more agile entities. Key Features and Functionality: - Custom AI Model Development: Crafting specialized AI models, including predictive analytics, recommendation systems, and image recognition, to meet specific business requirements. - AI-Powered Applications: Developing sophisticated web and mobile applications infused with AI, such as e-commerce platforms with intelligent product recommendations and expense trackers with automated categorization. - AI Chatbots: Building AI-driven chatbots for customer service, lead generation, and FAQ handling, ensuring 24/7 client engagement. - Automation Workflows: Automating repetitive tasks through AI-based workflows, including data entry, report generation, and customer outreach, to boost productivity. - Forecast Models: Creating intelligent forecasting models that predict future trends based on historical data, applicable in finance, sales, and inventory management. - System Integrations: Seamlessly integrating AI capabilities into existing tech infrastructures like CRM and ERP systems to enhance functionality. - Custom Software Development: Delivering tailor-made software solutions across web, mobile, and desktop platforms to address unique business challenges. - IT Consulting Services: Providing comprehensive IT assistance, from infrastructure planning to project management, guiding businesses through technological landscapes. - Cloud Computing Solutions: Offering cloud services to enhance scalability, efficiency, and cost-effectiveness for businesses of all sizes. - Cybersecurity Services: Implementing robust security measures, including network security and data encryption, to protect businesses from cyber threats. - Managed IT Services: Overseeing IT infrastructure management, allowing businesses to focus on core operations. - Software Testing and QA: Ensuring software reliability through extensive testing and quality assurance processes. Primary Value and Solutions Provided: Predict Expert AI empowers businesses to harness the transformative power of artificial intelligence, addressing challenges such as operational inefficiencies, data management complexities, and the need for predictive insights. By delivering customized AI solutions and integrating them seamlessly into existing systems, the company enables organizations to make informed decisions, automate routine tasks, and enhance customer engagement. This strategic adoption of AI not only streamlines operations but also drives innovation and competitive advantage in the marketplace. **Who Is the Company Behind Predict Expert AI?** - **Seller:** [Predict Expert AI](https://www.g2.com/sellers/predict-expert-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/predict-expert-ai/ (4 employees on LinkedIn®) ### 6. [Prediction Guard](https://www.g2.com/products/prediction-guard/reviews) Prediction Guard enables security-sensitive teams to deploy, operate, and govern generative AI without compromising data control or compliance. The platform is built for true private deployment — on-prem, air-gapped, hybrid, or cloud — and supports bring-your-own-model workflows so teams can run preferred open models behind their firewall. Security and governance are applied directly in the inference pipeline: Prediction Guard performs pre-model PII detection & anonymization, prompt-injection scoring and blocking, and post-model output validation to reduce leakage and hallucination risk. Admins get tamper-resistant audit logs, configurable policy rules, real-time alerts, and developer-friendly APIs and SDKs for MLOps integration. Prediction Guard is purpose-built for regulated industries (finance, healthcare, legal) and platform teams that need to scale private AI with operational controls and auditability. **Who Is the Company Behind Prediction Guard?** - **Seller:** [Prediction Guard](https://www.g2.com/sellers/prediction-guard) - **HQ Location:** Lafayette, Indiana - **LinkedIn® Page:** https://www.linkedin.com/company/prediction-guard/ (14 employees on LinkedIn®) ### 7. [Predict Now AI](https://www.g2.com/products/predict-now-ai/reviews) PredictNow.ai is a financial machine learning platform designed to enhance human decision-making in trading and asset management through its "Corrective AI" approach. By integrating advanced machine learning algorithms with existing trading strategies, it enables hedge funds and financial institutions to accurately forecast the probability of profitable trades, thereby optimizing portfolio performance without replacing human expertise. Key Features and Functionality: - Pre-Engineered Financial Features: Offers a suite of tailored input features specifically designed for the financial sector, enhancing error prediction and decision-making accuracy. - Conditional Portfolio Optimization (CPO): Utilizes a proprietary algorithm that dynamically adjusts asset allocations and trading parameters in response to current market conditions, optimizing portfolio performance. - No-Code Interface and API Integration: Provides an intuitive, no-code user interface along with API access, allowing seamless integration with existing systems and enabling users to apply machine learning predictions without prior programming knowledge. Primary Value and User Solutions: PredictNow.ai empowers hedge funds and financial institutions to refine their trading strategies by accurately predicting the profitability of trades. By leveraging big data and machine learning, it enhances decision-making processes, leading to improved risk management and strategic capital deployment. The platform's Corrective AI approach ensures that human expertise remains central, augmenting it with data-driven insights to drive greater financial success. **Who Is the Company Behind Predict Now AI?** - **Seller:** [Predictnow](https://www.g2.com/sellers/predictnow) - **HQ Location:** Niagara-on-the-Lake, CA - **LinkedIn® Page:** https://www.linkedin.com/company/predictnow-ai (2 employees on LinkedIn®) ### 8. [PredxBio](https://www.g2.com/products/predxbio/reviews) PredxBio is an AI-driven, tissue-based biomarker company that leverages spatial analytics and artificial intelligence to transform tumor biopsy images into predictive biomarkers. These biomarkers enhance oncology drug discovery, translational research, and clinical development. By integrating deep spatial analytics, microdomain biology, and tissue-based multi-omics, PredxBio delivers actionable insights that help pharmaceutical partners uncover mechanisms of response and resistance, guiding biomarker-driven clinical trial design. Their end-to-end platform supports tissue quality control, advanced spatial analysis, biomarker discovery, and translational deployment, making complex tumor biology accessible, interpretable, and ready for real-world clinical impact. Key Features and Functionality: - SpaceIQ™ Platform: PredxBio's decision-intelligence platform transforms complex tissue and multi-omic spatial data into trusted, explainable insights that guide drug development from early discovery through clinical programs and portfolio strategy. - Discovery & Mechanism Intelligence: Reveals response-defining tissue biology to inform target selection and mechanism-of-action hypotheses by uncovering biologically meaningful spatial patterns across cells, neighborhoods, and tissue architecture that traditional analyses may overlook. - Translational & Clinical Readiness: Defines predictive, explainable tissue-based biomarkers and stratifies patient populations based on tissue-level biology linked to therapeutic response, enabling discovery insights to translate into biomarker strategies and hypothesis-driven trial design. - Trial & Program-Scale Decision Support: Applies consistent, reproducible tissue intelligence across studies to guide trial strategy, refine inclusion criteria, support clearer go/no-go decisions, and enable learning to compound across programs and portfolios over time. Primary Value and Problem Solved: PredxBio addresses the challenge of translating complex tissue data into actionable insights for drug development. By identifying response-defining tissue patterns across spatial multi-omic data, the company enables earlier go/no-go decisions, stronger biomarker strategies, and more efficient clinical development. This approach helps pharmaceutical partners reveal mechanisms of response and resistance, guiding biomarker-driven clinical trial design and ultimately improving patient outcomes in oncology. **Who Is the Company Behind PredxBio?** - **Seller:** [PredxBio](https://www.g2.com/sellers/predxbio) - **HQ Location:** Pittsburgh, US - **LinkedIn® Page:** https://www.linkedin.com/company/33271742 (15 employees on LinkedIn®) ### 9. [Predyct](https://www.g2.com/products/predyct/reviews) Predyct specializes in delivering intelligence for industrial infrastructure through a scalable, wireless, and easy-to-install nano-engineered sensor network. This innovative system continuously monitors critical industrial assets and, when combined with a data-centric AI platform, provides actionable insights that lead to significant cost savings and promote sustainable operations across sectors such as renewable energy, oil & gas, petrochemical, utilities, and mining. Key Features and Functionality: - Nano-Engineered Sensors: These proprietary sensors continuously record asset conditions via permanent physical changes without requiring power, ensuring maintenance-free operation. - Wireless Data Transmission: The system employs low-power wireless data transmission to mobile devices or fixed gateways, facilitating remote monitoring without complex wiring. - Data-Centric AI Platform: Predyct's cloud-based platform utilizes high-fidelity hybrid analytics, combining physics-based models with machine learning to create operational digital twins. - Predictive Insights: The platform supports early anomaly detection, proactive planning, performance optimization, compliance monitoring, life extension, and sustainability initiatives. - Scalable Deployment: Designed for large-scale implementation, the system can be configured to meet specific application requirements, making it suitable for various industrial environments. Primary Value and Problem Solved: Predyct addresses the challenges of unplanned downtime, safety risks, and increased operational costs associated with asset degradation due to factors like cracking, fatigue, corrosion, and erosion. Traditional monitoring methods are often labor-intensive, costly, and provide limited data, hindering proactive maintenance and efficient operations. Predyct's solution offers a maintenance-free, easy-to-install monitoring system that delivers proactive insights, enabling industries to enhance uptime, reduce costs, and minimize emissions, thereby promoting more efficient and sustainable operations. **Who Is the Company Behind Predyct?** - **Seller:** [Predyct](https://www.g2.com/sellers/predyct) - **HQ Location:** Houston, US - **LinkedIn® Page:** https://www.linkedin.com/company/predyctio/ (1 employees on LinkedIn®) ### 10. [PrescientIQ](https://www.g2.com/products/prescientiq/reviews) PrescientIQ™ is the autonomous execution platform by MatrixLabX that replaces fragmented MarTech stacks with pre-trained AI agents — executing revenue, compliance, and operational workflows 24/7 without human prompting. The platform uses a four-stage Sense → Decide → Act → Learn loop powered by Anthropic Claude and Google Vertex AI, achieving 4× higher goal completion than AI copilot tools. Certified SOC 2 Type II, GDPR, and HIPAA compliant, PrescientIQ™ deploys in 5–15 business days with all processing contained within the Google Cloud perimeter. **Who Is the Company Behind PrescientIQ?** - **Seller:** [PrescientIQ](https://www.g2.com/sellers/prescientiq) - **Year Founded:** 2024 - **HQ Location:** Colchester, US - **LinkedIn® Page:** https://www.linkedin.com/company/prescientiq (1 employees on LinkedIn®) ### 11. [Prevision](https://www.g2.com/products/prevision/reviews) Prevision.io develops a full automated Machine Learning Plateform that increase productivity in datascience projects , reduce time to market to delivers accurate predictive models and put them in production, and delivers a full palet of explainability to understand the decisions of the models. The solution very easy to use does not need any knowledge of data modelisation, the Artificial Intelligence of the plateform build without humans all the predictive models with great accuracy. Business Analyst can use the product in self service mode without any datascientists. Datascientists can boost their productivity by using automation on data modeling, reducing production steps, and launch many test signal of their upstream datas to understand if the sourcing they have produces signal or not. Application developers can built themselves strong predictive models for all kind of their use. **Who Is the Company Behind Prevision?** - **Seller:** [Prevision.io](https://www.g2.com/sellers/prevision-io) - **Year Founded:** 2016 - **HQ Location:** Paris, FR - **LinkedIn® Page:** http://www.linkedin.com/company/prevision.io (1 employees on LinkedIn®) ### 12. [Prior Labs TabPFN](https://www.g2.com/products/prior-labs-tabpfn/reviews) We build tabular foundation models that supercharge data science teams working with spreadsheets and databases. **Who Is the Company Behind Prior Labs TabPFN?** - **Seller:** [Prior Labs](https://www.g2.com/sellers/prior-labs) - **Year Founded:** 2024 - **HQ Location:** Freiburg / Berlin, DE - **LinkedIn® Page:** https://www.linkedin.com/company/prior-labs (19 employees on LinkedIn®) ### 13. [ProActive Machine Learning](https://www.g2.com/products/proactive-machine-learning/reviews) ProActive Machine Learning (PML) from Activeeon is a data science automation platform that enables enterprises to: - Automate the complete data science lifecycle at scale, - Remove silos by creating a bridge between teams: DataOps, Data Science and DevOps, - Abstract application complexity by integrating all your favorite tools, - Abstract infrastructure complexity by connecting all your compute resources, - Allow easy communication between teams and unify the lifecycle. ProActive Machine Learning solution is designed to help you speed up the data science journey from extracting raw data to deploying models in production so that you can get the business advantages you are looking for. For data engineers: - Data connector tasks & workflows templates to automate and scale up data ingestion and data preparation pipelines. For data scientists: - AutoML to scale up the model tuning during experiments, - Jupyter Kernel & Python Connector to create AI workflows from code, - AI tasks & workflows template to automate AI pipelines to scale up parallel model training, validation and testing. For AI architects: - Model as a Service (MaaS) to deploy and expose AI models in production, enable model monitoring, alerting, data drift detection, scale up model deployment, - JupyterLab as a Service to deploy a JupyterLab instance on-demand, launch JupyterLab on specific compute nodes, - Job Analytics & Visualization as a Services to use your favorite tool to track and visualize metrics of your machine learning workflow, - Managed Services (KNIME, …) to launch your favorite tool on-demand. **Who Is the Company Behind ProActive Machine Learning?** - **Seller:** [ActiveEon](https://www.g2.com/sellers/activeeon) - **Year Founded:** 2007 - **HQ Location:** Sophia Antipolis, FR - **Twitter:** @activeeon (447 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/activeeon/ (19 employees on LinkedIn®) ### 14. [Probabl](https://www.g2.com/products/probabl/reviews) Probabl is a provider of open-source data science and machine learning solutions and services. **Who Is the Company Behind Probabl?** - **Seller:** [Probabl](https://www.g2.com/sellers/probabl) - **Year Founded:** 2023 - **HQ Location:** Paris, FR - **LinkedIn® Page:** https://fr.linkedin.com/company/probabl (44 employees on LinkedIn®) ### 15. [ProbeAI](https://www.g2.com/products/probeai/reviews) ProbeAI is an AI-powered copilot designed to assist data analysts in streamlining their workflow and enhancing productivity. By leveraging advanced artificial intelligence, ProbeAI simplifies complex SQL coding tasks, identifies relevant data tables, and adapts to specific business definitions, thereby reducing manual effort and minimizing errors. Key Features and Functionality: - Auto-Generation of Complex SQL Code: ProbeAI can generate intricate SQL queries based on user prompts, facilitating efficient data retrieval without the need for extensive manual coding. - Identification of Relevant Tables: The tool assists in pinpointing the most pertinent tables for a given query, streamlining the data analysis process. - Adaptation to Business-Specific Definitions: ProbeAI understands and incorporates unique business terminologies and definitions, ensuring that the generated queries align with organizational standards. - Support for Major Databases and Warehouses: The platform is compatible with leading databases and data warehouses, including BigQuery, Snowflake, MySQL, and PostgreSQL, offering flexibility across various data environments. Primary Value and Problem Solved: ProbeAI addresses the challenges data analysts face in writing and optimizing complex SQL queries. By automating code generation and error detection, it significantly reduces the time and effort required for data analysis tasks. This leads to increased efficiency, accuracy, and the ability to focus on deriving insights rather than managing code intricacies. **Who Is the Company Behind ProbeAI?** - **Seller:** [ProbeAI](https://www.g2.com/sellers/probeai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 16. [Prodify](https://www.g2.com/products/prodify/reviews) Prodify is an advanced AI-driven platform designed to streamline and enhance product development processes. By integrating cutting-edge artificial intelligence technologies, Prodify assists teams in efficiently managing product lifecycles, from ideation to market launch. Its intuitive interface and robust analytics empower organizations to make data-driven decisions, reducing time-to-market and improving product quality. Key Features and Functionality: - AI-Powered Insights: Leverages machine learning algorithms to analyze market trends and user feedback, providing actionable recommendations. - Collaborative Workspace: Offers a centralized platform for team collaboration, ensuring seamless communication and project tracking. - Automated Workflow Management: Streamlines task assignments and progress monitoring through intelligent automation. - Customizable Dashboards: Provides real-time analytics and performance metrics tailored to specific project needs. - Integration Capabilities: Easily integrates with existing tools and systems, enhancing workflow efficiency. Primary Value and User Solutions: Prodify addresses common challenges in product development by reducing inefficiencies and fostering innovation. It enables teams to quickly adapt to market changes, make informed decisions, and deliver high-quality products that meet customer expectations. By automating routine tasks and providing deep insights, Prodify allows organizations to focus on strategic initiatives, ultimately driving growth and competitive advantage. **Who Is the Company Behind Prodify?** - **Seller:** [Prodify](https://www.g2.com/sellers/prodify) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 17. [Profet AI AutoML](https://www.g2.com/products/profet-ai-automl/reviews) Profet AI is an Industry AI software Company, provides an end-to-end AutoML platform that enables manufacturing domain users to rapidly generate world-class AI models and applications for any use case at any time. **Who Is the Company Behind Profet AI AutoML?** - **Seller:** [Profet AI](https://www.g2.com/sellers/profet-ai) - **Year Founded:** 2018 - **HQ Location:** Xinyi District, TW - **LinkedIn® Page:** https://www.linkedin.com/company/profetai/ (61 employees on LinkedIn®) ### 18. [Profphet](https://www.g2.com/products/profphet/reviews) PrOFphet is an AI-powered chatbot designed specifically for OnlyFans creators, enabling them to automate and enhance their messaging with fans. By leveraging advanced artificial intelligence, PrOFphet crafts personalized messages that emulate the creator's unique style, fostering deeper connections and significantly increasing pay-per-view (PPV) sales. Key Features and Functionality: - Personalized Messaging: Generates messages that reflect the creator's tone and personality, ensuring authentic interactions. - Comprehensive Memory: Maintains a detailed history of fan interactions from the inception of the OnlyFans account, allowing for contextually relevant conversations. - Time Efficiency: Automates routine communications, freeing up creators to focus on content creation and other priorities. - Enhanced Fan Engagement: Encourages more frequent and meaningful interactions, leading to increased fan loyalty and higher revenue. Primary Value and User Solutions: PrOFphet addresses the challenge of managing extensive fan communications by providing an AI-driven solution that ensures timely, personalized, and engaging messages. This not only saves creators valuable time but also enhances fan satisfaction and boosts PPV sales, ultimately contributing to the creator's overall success on the platform. **Who Is the Company Behind Profphet?** - **Seller:** [PrOFphet- OF AI Chatbot](https://www.g2.com/sellers/profphet-of-ai-chatbot) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 19. [Profundo](https://www.g2.com/products/profundo/reviews) Profundo is an AI-powered research and reporting tool designed to automate data collection, analysis, and reporting processes. By leveraging advanced AI algorithms, it enables users to efficiently gather and analyze data, transforming manual tasks into automated insights. This allows individuals and organizations to focus more on learning and decision-making, enhancing productivity and accuracy in research endeavors. Key Features and Functionality: - Rapid Data Discovery: Automatically collects data from numerous web sources and libraries in real-time, streamlining the research process. - Deep Analysis: Utilizes sophisticated algorithms to identify patterns, trends, and insights within the gathered data. - Customized Reporting: Generates detailed and tailored reports based on analyzed data, catering to specific user needs. - Pre-Built Templates: Offers templates for search queries to provide targeted insights, ensuring reports are both accurate and informative. - Pay-as-You-Go Model: Employs a token-based system, allowing flexible and usage-based access to platform features. - Data Integration: Allows users to securely integrate and analyze their proprietary data alongside sourced data for comprehensive analysis. Primary Value and User Solutions: Profundo addresses the challenges of time-consuming and error-prone manual research by automating critical components of the research workflow. It serves a diverse user base, including individuals, academics, and industry professionals, by facilitating: - Accelerated Learning: Speeds up the research process, enabling users to quickly acquire and process information. - Enhanced Accuracy: Reduces human errors through AI-driven analysis, leading to more reliable outcomes. - User-Friendly Experience: Provides an intuitive interface that integrates seamlessly with existing tools, making it accessible to both novices and experts. By automating data-related tasks, Profundo empowers users to concentrate on critical thinking and informed decision-making, ultimately saving time and resources. **Who Is the Company Behind Profundo?** - **Seller:** [Profundo](https://www.g2.com/sellers/profundo) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 20. [Promigence](https://www.g2.com/products/promigence/reviews) Promigence is an advanced AI-driven platform designed to enhance business intelligence and decision-making processes. By leveraging cutting-edge machine learning algorithms, it analyzes vast datasets to uncover actionable insights, enabling organizations to make informed strategic decisions. The platform's intuitive interface ensures accessibility for users across various technical backgrounds, facilitating seamless integration into existing workflows. Key Features and Functionality: - Data Integration: Aggregates data from multiple sources, providing a unified view for comprehensive analysis. - Predictive Analytics: Utilizes machine learning models to forecast trends and outcomes, aiding in proactive decision-making. - Customizable Dashboards: Offers interactive dashboards that can be tailored to specific business needs, enhancing data visualization. - Automated Reporting: Generates real-time reports, reducing manual effort and ensuring timely information dissemination. - Scalability: Adapts to businesses of all sizes, accommodating growth and evolving data requirements. Primary Value and Solutions Provided: Promigence addresses the challenge of data overload by transforming complex datasets into clear, actionable insights. It empowers businesses to identify opportunities, mitigate risks, and optimize operations, ultimately driving growth and competitive advantage. By automating analytical processes, it reduces the time and resources spent on data interpretation, allowing organizations to focus on strategic initiatives. **Who Is the Company Behind Promigence?** - **Seller:** [Promigence](https://www.g2.com/sellers/promigence) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 21. [Property AI](https://www.g2.com/products/property-ai/reviews) Property AI is an innovative tool designed to simplify property investment by providing accurate data analysis and actionable insights. By inputting key property details such as price, location, and amenities, users receive instant evaluations on rental potential, return on investment (ROI), and occupancy rates. The platform also offers tailored advice to enhance property value and maximize investment returns, making it an essential resource for property investors seeking to optimize their portfolios. Key Features: - Property Assessment: Detailed analysis of property value and potential profitability. - Investment Insights: Information on payback periods, rental costs, and ROI. - Market Tips: Personalized recommendations to improve property value and profitability. - Detailed Reports: Comprehensive reports on property analysis and investment potential. The primary value of Property AI lies in its ability to eliminate the guesswork from property investment decisions. By leveraging automated data analysis, it empowers users to make informed choices, thereby enhancing their investment outcomes and maximizing rental yields. **Who Is the Company Behind Property AI?** - **Seller:** [Property AI](https://www.g2.com/sellers/property-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 22. [Prosperse - Stock Scanner](https://www.g2.com/products/prosperse-stock-scanner/reviews) Prosperse's Stock Screener is a powerful, customizable tool designed to streamline the stock selection process for traders and investors. By allowing users to create personalized scanners, it enables efficient identification of stocks that meet specific technical and fundamental criteria, thereby enhancing decision-making and trading performance. Key Features and Functionality: - Customizable Scanners: Utilize a no-code strategy builder to create scanners tailored to any technical or fundamental condition imaginable. - Real-Time Execution: Leverage live scanning technology to instantly identify stocks aligning with your selection criteria. - Automated Background Screening: Scanners run continuously in the background, providing real-time notifications when results are found. - Backtesting Capabilities: Test your scanners against historical data to evaluate and refine strategies without interrupting active scans. - All-in-One Dashboard: Manage charting, scan results, watchlists, and live positions in a centralized interface, offering real-time insights into your portfolio. Primary Value and User Solutions: Prosperse's Stock Screener addresses the challenge of efficiently identifying investment opportunities in a vast market. By automating the scanning process and providing real-time alerts, it saves users significant time and effort. The customizable nature of the tool ensures that both novice and experienced traders can tailor it to their unique strategies, leading to more informed decisions and improved trading outcomes. **Who Is the Company Behind Prosperse - Stock Scanner?** - **Seller:** [Prosperse](https://www.g2.com/sellers/prosperse) - **Year Founded:** 2022 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/prosperseapp/ (1 employees on LinkedIn®) ### 23. [Protai](https://www.g2.com/products/protai/reviews) Protai is an AI-powered drug discovery startup that integrates deep proteomics with machine learning to revolutionize the development of new therapies. By analyzing proteins in their native states, Protai aims to transform disease treatment by identifying unique disease drivers and developing innovative strategies for breakthrough therapies and improved patient outcomes. Key Features and Functionality: - AIMS™ Platform: Protai's proprietary drug discovery engine combines structural and functional mass spectrometry proteomics data with AI to uncover novel drug mechanisms and identify best-in-class drug candidates. - Target Redefinition: The platform maps disease-specific protein states to define precise, context-specific mechanisms of action, enabling selective inhibition of disease-relevant activities while preserving beneficial functions. - Structural Modeling: Utilizing structural proteomics techniques like cross-linking mass spectrometry (XL-MS) and hydrogen-deuterium exchange mass spectrometry (HDX-MS), AIMS™ determines disease-specific protein conformations. These are then modeled using structural AI to identify pockets suitable for targeted drug design. - Computational Drug Design: The platform facilitates the discovery of modulators for redefined targets in oncology and immunology, accelerating the development of precision medicines. Primary Value and Problem Solved: Protai addresses the challenges in drug discovery by providing a comprehensive understanding of protein networks and their dynamic interactions in health and disease. This approach allows for the identification of unique disease drivers and the development of targeted therapies, leading to more effective treatments and improved patient outcomes. By integrating proteomics with AI, Protai enhances the precision and efficiency of drug discovery, reducing the time and cost associated with bringing new drugs to market. **Who Is the Company Behind Protai?** - **Seller:** [Protai](https://www.g2.com/sellers/protai) - **Year Founded:** 2021 - **HQ Location:** Tel Aviv, IL - **LinkedIn® Page:** https://www.linkedin.com/company/protai-bio (27 employees on LinkedIn®) ### 24. [Provectus](https://www.g2.com/products/provectus-provectus/reviews) Provectus is an Artificial Intelligence (AI) consultancy and solutions provider dedicated to helping businesses achieve their objectives through AI integration. By offering tailored AI solutions, Provectus enables organizations to reimagine their operations and drive innovation. Key Features and Functionality: - Use Case Approach: Empowers businesses to implement AI-powered use cases, delivering quick and actionable results. - Platform Approach: Lays a comprehensive foundation to prepare businesses for AI transformation. - No License Fees: Provides solutions without restrictive proprietary IP agreements. - Cloud Deployment: Offers AI solutions deployable in the client's cloud environment, ensuring instant access for business users. - Open & Certified Architecture: Utilizes open and certified source code and architecture, eliminating black boxes and license fees. - Vendor Agnostic: Employs cloud-native solutions that minimize total cost of ownership without vendor lock-in. - Turnkey Solutions: Manages strategy, architecture, and implementation without white labeling or subcontracting. - AI Consulting & Customization: Includes solution integration, delivery, and training for technical teams to utilize and modify solutions effectively. Primary Value and Problem Solved: Provectus addresses the challenge of integrating AI into business operations by offering customized solutions that align with unique objectives and technical capabilities. By eliminating license fees, providing open architectures, and ensuring vendor-agnostic deployments, Provectus empowers organizations to adopt AI technologies seamlessly, driving innovation and achieving measurable business outcomes. **Who Is the Company Behind Provectus?** - **Seller:** [Provectus](https://www.g2.com/sellers/provectus-5625d395-7af1-463c-8be1-b2b5329cfaad) - **Year Founded:** 2010 - **HQ Location:** Palo Alto, CA - **LinkedIn® Page:** https://www.linkedin.com/company/provectus-it-inc/ (570 employees on LinkedIn®) ### 25. [PROWLER.io](https://www.g2.com/products/prowler-io/reviews) PROWLER.io is an AI company based in Cambridge, UK. We develop tools that help people make better business decisions. **Who Is the Company Behind PROWLER.io?** - **Seller:** [prowler.io](https://www.g2.com/sellers/prowler-io) - **Year Founded:** 2016 - **HQ Location:** Cambridge, GB - **LinkedIn® Page:** https://www.linkedin.com/company/secondmind-ai (68 employees on LinkedIn®) ## What Is Data Science and Machine Learning Platforms? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to Data Science and Machine Learning Platforms? - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) --- ## How Do You Choose the Right Data Science and Machine Learning Platforms? ### What You Should Know About Data Science and Machine Learning Platforms ### What are data science and machine learning (DSML) platforms? 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 data science, of which [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions. One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning. ### Types of DSML platforms Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy [machine learning models](https://www.g2.com/articles/what-is-machine-learning). However, they differ in terms of the data types supported and the method and manner of deployment.  **Cloud** **data science and machine learning platforms** With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both 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 that have been deployed. **On-premises** **data science and machine learning platforms** 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 several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as [HIPAA](https://www.g2.com/glossary/hipaa-definition), require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary. **Edge** **platforms** Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of [data centers](https://www.g2.com/glossary/data-center-definition) that process and store data locally before being sent to a centralized storage center or cloud. [Edge computing](https://learn.g2.com/trends/edge-computing) optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** ** ### What are the common features of data science and machine learning solutions? The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models. **Data preparation:** Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices. Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for [data cleansing](https://www.g2.com/articles/data-cleaning) and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start. **Model training:** Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are [supervised learning and unsupervised learning](https://www.g2.com/articles/supervised-vs-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 that 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. **Model deployment:** The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more. ### What are the benefits of using DSML engineering platforms? Through the use of data science and machine learning platforms, data scientists can gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working and provides them 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 can share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork. **Simplify and scale data science:** Many platforms are opening up these tools to a broader audience with easy-to-use features and drag-and-drop capabilities. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets. **Experimentation:** 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. Data science and machine learning vendors facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for [deep learning](https://www.g2.com/articles/deep-learning), which are algorithms or methods used to change the attributes of neural networks, such as weights and learning rate, to reduce losses, are also used in experimentation. ### Who uses data science and machine learning products? Data scientists are in high demand, but skilled professionals are in shortage. The skillset is varied and vast (for example, there is a need to understand various algorithms, advanced mathematics, programming skills, and more). Therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms increasingly include 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 them. The more robust platforms provide resources that help nontechnical users understand the models, the data involved, and the aspects of the business that 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:** 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 data science and machine learning platforms to bring AI into their organizations. **Professional data scientists:** Expert data scientists use these solutions to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment and 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 data science and machine learning platforms? Alternatives to data science and machine learning solutions can replace this type of software, either partially or completely: [AI & machine learning operationalization software](https://www.g2.com/categories/ai-machine-learning-operationalization) **:** Depending on the use case, businesses might consider AI and machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models. [Machine learning software](https://www.g2.com/categories/machine-learning) **:** Data science and machine learning platforms are great for the full-scale development of models, whether that be for [computer vision](https://learn.g2.com/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. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific ones, such as association rule learning, [Bayesian networks](https://www.g2.com/articles/artificial-intelligence-terms#:~:text=Bayesian%20network%3A%20also%20known%20as%20the%20Bayes%20network%2C%20Bayes%20model%2C%20belief%20network%2C%20and%20decision%20network%2C%20is%20a%20graph%2Dbased%20model%20representing%20a%20set%20of%20variables%20and%20their%20dependencies.%C2%A0), clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations look for point solutions. ### **Software and services related to data science and machine learning engineering platforms** Related solutions that can be used together with DSML 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 data science and machine learning 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 many 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, which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data ingested by data science and machine learning platforms. [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](https://www.g2.com/articles/natural-language-processing) 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](https://www.g2.com/articles/voice-recognition) and [natural language generation (NLG)](https://www.g2.com/categories/natural-language-generation-nlg), which converts data into understandable human language. Some examples of NLP uses include [chatbots](https://www.g2.com/categories/chatbots), translation applications, and [social media monitoring tools](https://www.g2.com/categories/social-media-listening-tools) that scan social media networks for mentions. ### Challenges with DSML platforms Software solutions can come with their own set of challenges.  **Data requirements:** A great deal of data is required for most AI algorithms to learn what is needed. 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 necessary actions. 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 by 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 DSML engineering 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:** AI is widely used in financial services, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With data science and machine learning software solutions, data science teams can build models with company data and deploy them to 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 choose the best data science and machine learning (DSML) platform #### Requirements gathering (RFI/RFP) for DSML platforms If a company is just starting out and looking to purchase its 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, it needs to 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 deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from a data science platform might be helpful. #### Compare DSML products **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 a thorough comparison, the user should demo each solution on the short list using the same use case and datasets. This will allow the business to evaluate like-for-like and see how each vendor compares against the competition. #### Selection of DSML platforms **Choose a selection team** Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interests, 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 to recommend 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. ### Cost of data science and machine learning platforms As mentioned above, data science and machine learning platforms are available as both on-premises and cloud solutions. Pricing between the two might differ, with the former often requiring more upfront infrastructure costs.  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 frequently not have as many features and may have usage caps. DSML 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 data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is 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 data science and machine learning platforms **How are DSML software tools 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 that be 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 DSML platform implementation?** It may require many people or 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, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together its data and begin the journey of data science, starting with proper data preparation and management. **What is the implementation process for data science and machine learning products?** In terms of implementation, it is typical for the platform to be deployed in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test its use of a personalization algorithm for a limited number of visitors to its site to understand better 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 is unsuccessful, the team can return to the drawing board to determine what went wrong. This will involve examining the training data and 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. **When should you implement DSML tools?** As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must make getting their data in order their top priority, 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.  ### Data science and machine learning 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 is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it. Using embedded AI inside software like [CRM](https://www.g2.com/categories/crm), [marketing automation](https://www.g2.com/categories/marketing-automation), and [analytics solutions](https://www.g2.com/categories/analytics-tools-software) allows us 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 same way cloud deployment and mobile capabilities have over the past decade. 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 provide [MLaaS](https://www.g2.com/articles/machine-learning-as-a-service) for other enterprises. Developers quickly take advantage of these prebuilt algorithms and solutions by feeding them their 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 companies continue to rely on these microservices and the need for AI increases. **Explainability** When it comes to machine learning algorithms, especially deep learning, it may be 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. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law and the GDPR. --- ## What Are the Most Common Questions About Data Science and Machine Learning Platforms? *AI-generated · Last updated: April 27, 2026* ### Leading machine learning services for enterprise Based on G2 reviews, enterprise teams often favor platforms that unify data preparation, model training, deployment, governance, and monitoring in one environment. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — unified ML lifecycle and deployment. - [Databricks](https://www.g2.com/products/databricks/reviews) — lakehouse workflows with collaborative notebooks. - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — large-scale analytics with governance. - [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews) — governed AI development for enterprises. ### Top-rated software for data analysis in SaaS industry Based on G2 reviews, buyers in software environments often prioritize platforms that shorten analysis cycles, support collaboration, and reduce tool switching. - [Hex](https://www.g2.com/products/hex-tech-hex/reviews) — SQL, Python, and dashboarding together. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — end-to-end ML workflows in one place. - [Databricks](https://www.g2.com/products/databricks/reviews) — scalable analytics and ML collaboration. - [Deepnote](https://www.g2.com/products/deepnote/reviews) — collaborative notebooks for team analysis. ### Which platform offers the best machine learning solutions Based on G2 reviews, the strongest options depend on whether your team values unified workflows, low-code model building, notebook collaboration, or governance. - [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) — managed training, deployment, and monitoring. - [Databricks](https://www.g2.com/products/databricks/reviews) — engineering, analytics, and ML together. - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — advanced analytics with strong controls. - [Anaconda Platform](https://www.g2.com/products/anaconda-platform/reviews) — reproducible environments and package management. ### What are data science and machine learning platforms used for According to verified users, data science and machine learning platforms are used to centralize the work of preparing data, building models, testing ideas, deploying models, and sharing results. Reviews repeatedly mention workflow simplification as a major benefit: teams can reduce tool switching, automate repetitive preparation tasks, and move from experimentation to production with less manual setup. Buyers also use these platforms for dashboards, forecasting, predictive modeling, model monitoring, collaboration across technical and non-technical teams, and connecting data from warehouses, cloud systems, spreadsheets, or operational tools. Common buyer concerns in the reviews include learning curve, documentation quality, cost visibility, and performance on very large workloads. ### How do teams use data science and machine learning platforms for collaboration According to verified users, collaboration is one of the most practical reasons teams adopt these platforms. Reviews describe analysts, data scientists, and engineers working in shared notebooks, common environments, and governed workspaces so they can move from raw data to analysis, visualizations, and deployed models without passing files back and forth. Teams also mention easier sharing of dashboards, published apps, reusable workflows, and reproducible environments. In several reviews, this reduces friction between technical and non-technical stakeholders because results can be reviewed, discussed, and reused in one place. The strongest collaboration themes in the recent reviews are shared notebooks, consistent environments, versioned workflows, and easier handoffs into production.