# Best Data Science and Machine Learning Platforms - Page 23 *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. ## How Many Data Science and Machine Learning Platforms Products Does G2 Track? **Total Products under this Category:** 820 ## How Does G2 Rank Data Science and Machine Learning Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 13,000+ Authentic Reviews - 820+ 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:** [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/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** ### ILUM Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible. [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=1416491&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&secure%5Btoken%5D=e6e33bd39d1842aefe846d6ef229b77f22809505929b64d78c52b6fc09392b97&secure%5Burl%5D=https%3A%2F%2Filum.cloud%2F%3Futm%3Dg2&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026? ### 1. [Olostep](https://www.g2.com/products/olostep/reviews) Built to power the Web's second user, Olostep (olostep.com) is the best web search, scraping and crawling API for AI. Olostep is powering the world's leading AI startups and agents. It transforms complex, JavaScript-heavy websites into clean, structured, LLM-ready data. The API returns formats like Markdown, JSON, HTML, PDF, and screenshots. Olostep is the most reliable and cost-effective solution on the market, suited for scalable business needs. Olostep is one of the few solutions on the market that does not strictly require a monthly subscription. You can buy one-time credits that are valid for 6 months. Get clean data for your AI from any website with Olostep. Test it for free. No credit card required. **Who Is the Company Behind Olostep?** - **Seller:** [Olostep](https://www.g2.com/sellers/olostep) - **Year Founded:** 2024 - **HQ Location:** Dover, US - **LinkedIn® Page:** https://www.linkedin.com/company/olostep/ (5 employees on LinkedIn®) ### 2. [One Codex](https://www.g2.com/products/one-codex/reviews) One Codex is a comprehensive platform designed for microbiome sequencing and analysis, offering researchers and clinicians rapid, accurate insights into complex metagenomic samples. By integrating an extensive microbial reference database with advanced analytical tools, One Codex streamlines the process from sample collection to actionable results, facilitating a deeper understanding of microbial communities. Key Features and Functionality: - Extensive Microbial Reference Database: Access the world's largest curated collection of microbial genomes, enhancing the accuracy of taxonomic and functional analyses. - Intuitive Web-Based Interface: Navigate through data effortlessly with a user-friendly platform suitable for both bench scientists and computational biologists. - Rapid Turnaround Times: Benefit from automated sequencing and analysis pipelines that deliver results in as little as two weeks for sequencing and minutes for analyses. - Comprehensive Analysis Tools: Utilize advanced algorithms for shotgun and amplicon analyses, sample management, and collaborative features to facilitate efficient research workflows. - Secure and Compliant Environment: Ensure data security and compliance with regulatory standards, including GxP and HIPAA, reducing the compliance burden for users. Primary Value and User Solutions: One Codex addresses the challenges of microbiome research by providing a seamless, end-to-end solution for sequencing and data analysis. Its platform accelerates research timelines, ensures reproducibility, and offers a secure environment for data management. By simplifying complex bioinformatics processes, One Codex empowers users to focus on scientific discovery and clinical applications, ultimately advancing the understanding of microbial ecosystems and their impact on health and disease. **Who Is the Company Behind One Codex?** - **Seller:** [One Codex](https://www.g2.com/sellers/one-codex) - **Year Founded:** 2014 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/onecodex/ (892 employees on LinkedIn®) ### 3. [Oneconnectsolutions](https://www.g2.com/products/oneconnectsolutions/reviews) One Connect Solutions is a comprehensive data integration and analytics platform designed to empower organizations to effectively manage, analyze, and derive actionable insights from their data. By offering advanced tools for data transformation, automated machine learning, and semantic analytics, the platform enables users to make informed decisions and drive meaningful outcomes. With a focus on democratizing access to sophisticated analytics, One Connect Solutions caters to businesses of all sizes, facilitating data-driven strategies that enhance growth and efficiency. Key Features and Functionality: - Data Transformation: Utilize advanced tools to process and transform data, extracting meaningful insights for better decision-making. - Auto Machine Learning: Leverage automated machine learning algorithms to detect patterns and make predictions without the need for complex coding. - Semantic Analytics: Apply semantic analysis to understand the context and meaning within data, leading to deeper insights. - Spreev AI Agent: Interact with databases through intelligent conversations, allowing for natural language queries and instant insights without writing SQL. - No-Code/Low-Code Platform: Empower teams with advanced machine learning capabilities without the complexity of traditional coding, enabling seamless data integration and transformation. Primary Value and Solutions Provided: One Connect Solutions addresses the challenge of managing and analyzing vast amounts of data by providing a unified platform that simplifies data integration, transformation, and analysis. By offering no-code/low-code tools and AI-powered features, it enables organizations to: - Make data-driven decisions that drive growth and efficiency. - Enhance customer engagement through personalized insights. - Improve operational performance by automating processes and applying predictive analytics. - Democratize access to advanced analytics, allowing teams without extensive technical expertise to harness the power of data. In essence, One Connect Solutions transforms how organizations understand and leverage their data, enabling them to make smarter decisions and achieve meaningful outcomes. **Who Is the Company Behind Oneconnectsolutions?** - **Seller:** [SPREEV](https://www.g2.com/sellers/spreev) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 4. [Onnix](https://www.g2.com/products/onnix/reviews) Onnix is an AI-powered co-pilot tailored for banking professionals, designed to enhance productivity by automating core workflows. By integrating advanced AI and data science tools into a user-friendly, no-code platform, Onnix enables banking teams to generate accurate answers and deliverables swiftly. The platform streamlines tasks such as creating personalized slide decks, conducting data analyses, and querying data sources, allowing users to focus more on strategic insights and decision-making. Key Features and Functionality: - Personalized Slide Decks: Onnix learns from previous presentations to assemble slide decks tailored to managing director preferences, significantly reducing the time required for deck creation and iteration. - Excel Analysis: Users can perform data analyses and generate outputs through simple prompts. Onnix integrates seamlessly with data providers like FactSet and CapIQ, enabling teams to derive deep insights without the need for coding expertise. - Data Source Querying: The platform allows users to query information from integrated data sources instantly, providing answers with traceable references. This feature is particularly beneficial during live client interactions or when specific data points are required promptly. Primary Value and User Solutions: Onnix addresses common bottlenecks in banking workflows by automating repetitive tasks and facilitating rapid iterations. For senior teams, it offers the ability to quickly generate and adjust presentations and analyses, enhancing responsiveness to client needs. Junior teams benefit from the platform's capacity to handle complex data analyses and bespoke client requests efficiently. Overall, Onnix empowers banking professionals to focus on delivering strategic insights and high-quality client service, thereby driving productivity and operational excellence. **Who Is the Company Behind Onnix?** - **Seller:** [Bolto](https://www.g2.com/sellers/bolto) - **Year Founded:** 2023 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/boltohq/ (11 employees on LinkedIn®) ### 5. [On Target AI](https://www.g2.com/products/on-target-ai/reviews) On Target AI is an advanced artificial intelligence platform designed to enhance business decision-making and operational efficiency. By leveraging cutting-edge machine learning algorithms, it provides actionable insights that help organizations optimize their strategies and achieve their objectives more effectively. Key features and functionality of On Target AI include: - Predictive Analytics: Utilizes historical data to forecast future trends, enabling proactive decision-making. - Data Integration: Seamlessly combines data from various sources, offering a unified view for comprehensive analysis. - Customizable Dashboards: Provides intuitive dashboards that can be tailored to display key performance indicators relevant to specific business needs. - Automated Reporting: Generates detailed reports automatically, saving time and reducing the potential for human error. - Scalability: Adapts to businesses of all sizes, ensuring consistent performance as data volumes grow. The primary value of On Target AI lies in its ability to transform raw data into meaningful insights, empowering businesses to make informed decisions swiftly. By automating complex analytical processes, it reduces the burden on human resources and minimizes the risk of errors. This leads to improved operational efficiency, cost savings, and a competitive edge in the market. **Who Is the Company Behind On Target AI?** - **Seller:** [On Target AI](https://www.g2.com/sellers/on-target-ai) - **Year Founded:** 2025 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/ontargetai (4 employees on LinkedIn®) ### 6. [OpenDoc AI](https://www.g2.com/products/opendoc-ai/reviews) OpenDoc AI is an innovative platform designed to democratize data science by enabling users, regardless of technical expertise, to automate data workflows, generate insights, and build custom AI models. By allowing users to interact with data through natural language queries, OpenDoc AI simplifies complex data analysis tasks, making them accessible to everyone. Key Features and Functionality: - Natural Language Interface: Users can ask analytical questions in plain English, eliminating the need for coding or technical knowledge. - Generative AI Workflows: Automate data tasks by providing simple prompts, allowing for the creation and execution of complex workflows without coding. - Custom Analytics Queries: Generate data insights through conversational interactions, empowering users to extract valuable information effortlessly. - Integration with Multiple Databases: Seamlessly connect to various databases, including PostgreSQL, MySQL, Snowflake, Amazon Redshift, and BigQuery. - Support for Various Data Formats: Handle multiple file types such as CSV, IMG, PDF, XLSX, and HTML, ensuring flexibility in data processing. - AI-Powered Data Analysis and Visualization: Employ advanced AI and machine learning algorithms to extract valuable insights and create visual representations of data. - Predictive Analytics Capabilities: Utilize AI to forecast trends and outcomes, aiding in proactive decision-making. - Custom Reporting Features: Generate tailored reports to meet specific business needs and objectives. - Automated Data Processing and Analysis: Streamline repetitive tasks, reducing manual effort and increasing efficiency. - Pattern and Anomaly Detection: Identify trends and outliers within datasets to uncover hidden insights. - Easy Integration with Existing Business Applications: Ensure seamless incorporation into current workflows and systems. - SOC2 Compliance in Progress: Commitment to enhanced data security and compliance standards. Primary Value and User Solutions: OpenDoc AI addresses the challenge of making data science accessible to non-technical users by providing an intuitive platform that simplifies data analysis and workflow automation. By enabling natural language interactions and automating complex tasks, it empowers businesses to make informed decisions quickly and efficiently. The platform's integration capabilities and support for various data formats ensure that it fits seamlessly into existing workflows, enhancing productivity and collaboration across teams. **Who Is the Company Behind OpenDoc AI?** - **Seller:** [OpenDoc AI](https://www.g2.com/sellers/opendoc-ai) - **Year Founded:** 2023 - **HQ Location:** new york , US - **LinkedIn® Page:** https://www.linkedin.com/company/93849705/ (1 employees on LinkedIn®) ### 7. [Openmodeldb](https://www.g2.com/products/openmodeldb/reviews) Openmodeldb is an open-source platform designed to facilitate the sharing, discovery, and reuse of machine learning models. It provides a centralized repository where data scientists and developers can upload, manage, and access a diverse range of pre-trained models across various domains. By promoting collaboration and knowledge sharing, Openmodeldb aims to accelerate the development and deployment of machine learning solutions. Key Features and Functionality: - Model Repository: A comprehensive collection of machine learning models, enabling users to browse, search, and download models suited to their specific needs. - Version Control: Tracks different versions of models, allowing users to access previous iterations and monitor changes over time. - Metadata Management: Stores detailed information about each model, including architecture, training data, performance metrics, and usage guidelines. - Community Collaboration: Encourages contributions from the global machine learning community, fostering an environment of shared knowledge and continuous improvement. - Integration Support: Provides APIs and tools for seamless integration with various machine learning frameworks and deployment platforms. Primary Value and User Solutions: Openmodeldb addresses the challenge of model reproducibility and accessibility in the machine learning community. By offering a centralized platform for model sharing, it reduces redundant efforts, accelerates innovation, and promotes best practices. Users benefit from easy access to a wide array of models, facilitating rapid prototyping and deployment of machine learning applications. Additionally, the platform's emphasis on collaboration and transparency enhances the overall quality and reliability of shared models. **Who Is the Company Behind Openmodeldb?** - **Seller:** [Openmodeldb](https://www.g2.com/sellers/openmodeldb) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 8. [Oracle BPM Suite](https://www.g2.com/products/oracle-bpm-suite/reviews) Oracle Business Process Management (BPM) Suite is a comprehensive platform designed to model, automate, and optimize business processes, enabling organizations to enhance operational efficiency and agility. By facilitating seamless collaboration between business and IT teams, Oracle BPM Suite empowers users to design, execute, and monitor end-to-end processes, ensuring alignment with strategic objectives and compliance requirements. Key Features and Functionality: - Visual Process Modeling: Utilizes an intuitive drag-and-drop interface for creating and modifying business processes, allowing both technical and non-technical users to design workflows effectively. - Workflow Automation: Automates repetitive tasks and approval workflows across various systems, streamlining operations and reducing manual intervention. - Business Rules Engine: Enables the creation and management of complex decision rules, ensuring consistent and compliant decision-making processes. - Process Analytics: Provides real-time insights into process performance and bottlenecks, facilitating continuous improvement and informed decision-making. - Seamless Integration: Offers prebuilt connectors and integrations with platforms like Oracle Fusion Cloud ERP, Salesforce, and Jira, unifying processes across SaaS and on-premises applications. Primary Value and Problem Solved: Oracle BPM Suite addresses the challenges of fragmented and inefficient business processes by providing a unified platform for process management. It enables organizations to rapidly automate approval workflows, simplify complex operations, and ensure compliance through reusable business rules and prebuilt integrations. By offering a low-code design environment, it empowers business users to take control of process improvement initiatives, reducing dependency on IT and accelerating innovation. Ultimately, Oracle BPM Suite enhances operational efficiency, agility, and collaboration, driving better business outcomes. **Who Is the Company Behind Oracle BPM Suite?** - **Seller:** [Oracle](https://www.g2.com/sellers/oracle) - **Year Founded:** 1977 - **HQ Location:** Austin, TX - **Twitter:** @Oracle (827,981 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (199,301 employees on LinkedIn®) - **Ownership:** NYSE:ORCL ### 9. [Orca-Tool](https://www.g2.com/products/orca-tool/reviews) Orca-Tool is a comprehensive software solution designed to streamline and enhance the efficiency of data analysis and visualization processes. It offers a user-friendly interface that allows both novice and experienced users to perform complex data operations with ease. By integrating advanced analytical tools with intuitive design, Orca-Tool empowers users to derive meaningful insights from their data, facilitating informed decision-making and strategic planning. Key Features and Functionality: - Data Integration: Seamlessly import and combine data from multiple sources, ensuring a unified dataset for analysis. - Advanced Analytics: Utilize a suite of analytical tools, including statistical analysis, predictive modeling, and machine learning algorithms, to uncover patterns and trends. - Interactive Visualization: Create dynamic and interactive charts, graphs, and dashboards that make data interpretation straightforward and engaging. - Collaboration Tools: Share insights and reports with team members in real-time, fostering a collaborative environment for data-driven decision-making. - Customization: Tailor the tool's functionalities to meet specific industry needs, ensuring relevance and applicability across various sectors. Primary Value and User Solutions: Orca-Tool addresses the common challenges associated with data analysis, such as data silos, complex analytical processes, and the need for specialized skills. By providing an integrated platform that simplifies data handling and analysis, it enables organizations to: - Enhance Productivity: Reduce the time and effort required for data processing and analysis, allowing teams to focus on strategic initiatives. - Improve Accuracy: Minimize human errors through automated processes and standardized workflows, leading to more reliable results. - Facilitate Informed Decisions: Equip decision-makers with clear and actionable insights, supporting evidence-based strategies and operations. In summary, Orca-Tool serves as a vital asset for organizations seeking to harness the full potential of their data, transforming raw information into valuable knowledge that drives success. **Who Is the Company Behind Orca-Tool?** - **Seller:** [Orca-Tool](https://www.g2.com/sellers/orca-tool) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 10. [Origon AI](https://www.g2.com/products/origon-ai/reviews) Origon AI is an advanced artificial intelligence platform designed to streamline and enhance business operations through intelligent automation and data-driven insights. By leveraging cutting-edge machine learning algorithms, Origon AI empowers organizations to optimize processes, improve decision-making, and drive innovation across various industries. Key Features and Functionality: - Intelligent Automation: Automates repetitive tasks, reducing manual effort and increasing operational efficiency. - Data Analytics: Provides comprehensive data analysis tools to uncover valuable insights and trends. - Customizable Solutions: Offers tailored AI models to meet specific business needs and objectives. - Scalability: Adapts to businesses of all sizes, ensuring seamless integration and growth. - User-Friendly Interface: Features an intuitive design for easy navigation and accessibility. Primary Value and Solutions: Origon AI addresses the challenge of managing complex business processes by introducing intelligent automation and insightful analytics. It enables organizations to make informed decisions, reduce operational costs, and enhance productivity. By integrating Origon AI, businesses can stay competitive in a rapidly evolving market, fostering innovation and achieving sustainable growth. **Who Is the Company Behind Origon AI?** - **Seller:** [Origon AI](https://www.g2.com/sellers/origon-ai) - **HQ Location:** Palo Alto, US - **LinkedIn® Page:** https://www.linkedin.com/company/origon-ai (408 employees on LinkedIn®) ### 11. [OsmoAI](https://www.g2.com/products/osmoai/reviews) OsmoAI is an advanced artificial intelligence platform designed to revolutionize the way businesses analyze and interpret complex data. By leveraging cutting-edge machine learning algorithms, OsmoAI enables organizations to extract meaningful insights, automate decision-making processes, and enhance operational efficiency. Its intuitive interface and robust analytical tools make it accessible to both technical and non-technical users, facilitating seamless integration into existing workflows. Key Features and Functionality: - Data Integration: OsmoAI seamlessly connects with various data sources, allowing for comprehensive data aggregation and analysis. - Advanced Analytics: The platform offers sophisticated analytical tools, including predictive modeling, trend analysis, and anomaly detection. - Automated Reporting: Users can generate detailed reports automatically, saving time and ensuring accuracy in data interpretation. - Customizable Dashboards: OsmoAI provides interactive dashboards that can be tailored to display key performance indicators relevant to specific business needs. - Scalability: Designed to handle large datasets, OsmoAI scales efficiently to meet the demands of growing businesses. Primary Value and Solutions Provided: OsmoAI addresses the challenge of managing and interpreting vast amounts of data by providing a streamlined, user-friendly platform that transforms raw data into actionable insights. It empowers businesses to make informed decisions quickly, identify opportunities for growth, and mitigate potential risks. By automating routine analytical tasks, OsmoAI reduces the burden on human resources, allowing teams to focus on strategic initiatives. Ultimately, OsmoAI enhances productivity, drives innovation, and supports data-driven decision-making across various industries. **Who Is the Company Behind OsmoAI?** - **Seller:** [OsmoAI](https://www.g2.com/sellers/osmoai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 12. [Ottogrid](https://www.g2.com/products/ottogrid/reviews) Ottogrid is an advanced data automation platform designed to streamline and enhance data enrichment and extraction processes for businesses. By integrating with trusted data providers like Hunter.io, Apollo, Proxycurl, and Crunchbase, Ottogrid delivers accurate and up-to-date information, enabling organizations to automate complex tasks at scale with improved accuracy and reliability. Key Features and Functionality: - Data Enrichment Tools: Ottogrid offers comprehensive tools for enriching data related to companies, individuals, and contacts. Users can gather professional information, retrieve detailed company data, identify individuals in specific roles, and validate email addresses, all through a unified interface. - Automations: The platform enables users to create automated workflows that trigger actions based on specific events within their data tables. For instance, upon completion of a data enrichment process, Ottogrid can automatically send real-time notifications to a Slack channel, keeping teams informed without manual intervention. - Integrations: Ottogrid seamlessly connects with various external tools and systems, including Slack, Airtable, HubSpot, and Salesforce. These integrations facilitate the synchronization and centralization of data workflows, enhancing efficiency and collaboration across platforms. - Knowledge Bases: Users can create and manage knowledge bases containing text and file uploads, providing additional context for AI executions. This feature ensures that the AI considers relevant information when generating results, leading to more accurate and context-aware outputs. Primary Value and Problem Solved: Ottogrid addresses the challenges associated with manual data research and enrichment by automating these processes, thereby saving time and reducing errors. Its scalable architecture allows businesses to handle large-scale data tasks efficiently, making it an ideal solution for organizations seeking to enhance their data-centric workflows. By providing tools for data enrichment, automation, and integration, Ottogrid empowers users to focus on strategic decision-making rather than time-consuming manual data handling. **Who Is the Company Behind Ottogrid?** - **Seller:** [Cognosys](https://www.g2.com/sellers/cognosys) - **Year Founded:** 2023 - **HQ Location:** Vancouver, CA - **LinkedIn® Page:** https://www.linkedin.com/company/cognosysai-inc (3 employees on LinkedIn®) ### 13. [outerspan](https://www.g2.com/products/outerspan/reviews) OuterSpan is a comprehensive platform designed to streamline and enhance the management of complex projects and workflows. It offers a suite of tools that facilitate efficient collaboration, task automation, and real-time progress tracking, enabling teams to deliver projects on time and within budget. Key Features and Functionality: - Project Management: Provides intuitive tools for planning, scheduling, and monitoring project tasks and milestones. - Collaboration Tools: Enables seamless communication and document sharing among team members, fostering a collaborative work environment. - Task Automation: Automates repetitive tasks to increase efficiency and reduce manual errors. - Real-Time Analytics: Offers dashboards and reports that provide insights into project performance and resource utilization. - Integration Capabilities: Supports integration with various third-party applications to enhance functionality and adaptability. Primary Value and User Solutions: OuterSpan addresses the challenges of managing complex projects by providing a centralized platform that enhances team collaboration, automates routine tasks, and delivers real-time insights. This leads to improved productivity, better resource management, and successful project outcomes for organizations of all sizes. **Who Is the Company Behind outerspan?** - **Seller:** [outerspan](https://www.g2.com/sellers/outerspan) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 14. [Outlierdb](https://www.g2.com/products/outlierdb/reviews) Outlier Database is a specialized platform designed to enhance the learning and application of Brazilian Jiu-Jitsu (BJJ) techniques. It offers a comprehensive collection of sequences, resources, and highlights that cater to practitioners aiming to deepen their understanding and improve their skills in BJJ. Key Features and Functionality: - Extensive Technique Library: Provides a vast array of BJJ techniques, including detailed sequences and instructional resources, covering various positions, submissions, and transitions. - Curated Highlights: Features curated highlights from notable BJJ practitioners, offering insights into effective strategies and movements. - User-Friendly Interface: Designed with an intuitive interface that allows users to easily navigate through content, search for specific techniques, and access related resources. - Regular Updates: Continuously updated with new content to ensure users have access to the latest techniques and instructional materials. Primary Value and User Solutions: Outlier Database addresses the need for a centralized, accessible repository of BJJ knowledge. It solves the challenge practitioners face in finding reliable and organized instructional content by offering a platform where they can systematically study and refine their techniques. Whether for beginners seeking foundational knowledge or advanced practitioners aiming to expand their repertoire, Outlier Database serves as a valuable tool for continuous learning and skill enhancement in Brazilian Jiu-Jitsu. **Who Is the Company Behind Outlierdb?** - **Seller:** [Outlier Database](https://www.g2.com/sellers/outlier-database) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 15. [Outseek](https://www.g2.com/products/outseek/reviews) Outseek is a comprehensive financial terminal designed to empower investors and financial professionals with real-time market data, advanced analytics, and intuitive tools for informed decision-making. By integrating diverse financial information into a single platform, Outseek streamlines the investment research process, enhancing efficiency and accuracy. Key Features and Functionality: - Real-Time Market Data: Access up-to-the-minute information on stocks, bonds, commodities, and other financial instruments. - Advanced Analytics: Utilize sophisticated analytical tools to assess market trends, perform technical analysis, and develop investment strategies. - Customizable Dashboards: Tailor the interface to display relevant data and insights, aligning with individual preferences and workflows. - News Aggregation: Stay informed with curated financial news from reputable sources, providing context to market movements. - Portfolio Management: Monitor and manage investment portfolios with tools for performance tracking and risk assessment. Primary Value and User Solutions: Outseek addresses the need for a centralized, efficient platform that consolidates critical financial information and analytical tools. By offering real-time data, customizable interfaces, and comprehensive analytics, it enables users to make informed investment decisions swiftly. This integration reduces the time spent navigating multiple sources, thereby enhancing productivity and decision-making accuracy for investors and financial professionals. **Who Is the Company Behind Outseek?** - **Seller:** [Outseek Financial Terminal](https://www.g2.com/sellers/outseek-financial-terminal) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 16. [Overstand Labs](https://www.g2.com/products/overstand-labs/reviews) Overstand Labs offers a comprehensive suite of tools designed to enhance data analysis and visualization capabilities for businesses and researchers. Their platform integrates advanced analytics, machine learning algorithms, and interactive dashboards to transform complex datasets into actionable insights. By streamlining data processing and interpretation, Overstand Labs empowers users to make informed decisions efficiently. Key Features and Functionality: - Advanced Analytics: Utilize sophisticated algorithms to uncover patterns and trends within large datasets. - Machine Learning Integration: Implement predictive models to forecast outcomes and inform strategic planning. - Interactive Dashboards: Create customizable visual representations of data for intuitive analysis and reporting. - Data Integration: Seamlessly connect with various data sources to consolidate information in one platform. - Collaboration Tools: Share insights and collaborate with team members in real-time to enhance decision-making processes. Primary Value and Solutions: Overstand Labs addresses the challenge of managing and interpreting vast amounts of data by providing a user-friendly platform that simplifies complex analyses. By offering tools that automate data processing and visualization, it reduces the time and expertise required to derive meaningful insights. This enables organizations to make data-driven decisions swiftly, improving operational efficiency and strategic outcomes. **Who Is the Company Behind Overstand Labs?** - **Seller:** [Overstand Labs](https://www.g2.com/sellers/overstand-labs) - **HQ Location:** N/A - **LinkedIn® Page:** https://linkedin.com/company/overstand-labs (939 employees on LinkedIn®) ### 17. [Pagaya](https://www.g2.com/products/pagaya-pagaya/reviews) Pagaya is a global technology company that leverages artificial intelligence (AI) and machine learning to transform the financial services ecosystem. By integrating its proprietary AI-driven solutions, Pagaya enables financial institutions to expand their credit offerings, approve more customers, and manage risk effectively. The company's network connects over 31 lending partners with more than 135 institutional investors, facilitating seamless loan origination and funding processes. Key Features and Functionality: - AI-Powered Credit Analysis: Utilizes advanced machine learning models trained on extensive loan application data to assess creditworthiness, allowing for real-time, data-driven lending decisions. - Seamless API Integration: Offers easy integration with existing loan origination systems, enabling instant submission and processing of customer loan applications without latency. - Diverse Loan Product Support: Supports a wide range of loan products, including personal loans, auto loans, and point-of-sale financing, allowing partners to expand their lending portfolios. - Built-in Compliance and Security: Ensures adherence to U.S. regulations, fair lending practices, and data security standards, providing partners with a compliant and secure lending framework. Primary Value and Solutions Provided: Pagaya addresses the challenge of limited credit access by enabling financial institutions to approve more borrowers without increasing credit risk. By expanding the credit box, partners can retain more customers, grow their portfolios, and deepen customer relationships. The AI-driven approach ensures that lending decisions are both efficient and accurate, leading to higher customer satisfaction and long-term retention. **Who Is the Company Behind Pagaya?** - **Seller:** [Pagaya](https://www.g2.com/sellers/pagaya) - **Year Founded:** 2016 - **HQ Location:** New York, New York, United States - **LinkedIn® Page:** https://www.linkedin.com/company/pagaya (469 employees on LinkedIn®) ### 18. [Pandas-Ai](https://www.g2.com/products/pandas-ai/reviews) PandasAI is an innovative Python library that enhances data analysis by integrating generative AI capabilities directly into pandas dataframes. This tool allows users to interact with their data using natural language queries, streamlining the process of data exploration and insight generation. Beyond querying, PandasAI offers functionalities to visualize data through graphs, cleanse datasets by addressing missing values, and enhance data quality through feature generation, making it a comprehensive tool for data scientists and analysts. Key Features and Functionality: - Natural Language Querying: Enables users to ask questions directly to their data in plain English, eliminating the need for complex SQL or Python code. - Data Visualization: Automatically generates graphs and charts to represent data insights visually. - Data Cleansing: Identifies and addresses missing values within datasets to improve data integrity. - Feature Generation: Enhances datasets by creating new features that can lead to more robust analyses. - Data Connectors: Supports connections to various data sources, including CSV, XLSX, PostgreSQL, MySQL, BigQuery, Databricks, and Snowflake, facilitating seamless data integration. Primary Value and Problem Solved: PandasAI democratizes data analysis by allowing users to interact with their datasets through natural language, significantly reducing the technical barrier associated with traditional data querying methods. This approach not only accelerates the data analysis process but also makes it more accessible to individuals without extensive programming or SQL expertise. By automating tasks such as data visualization, cleansing, and feature generation, PandasAI empowers users to derive meaningful insights more efficiently, thereby enhancing decision-making processes across various domains. **Who Is the Company Behind Pandas-Ai?** - **Seller:** [Pandas ai](https://www.g2.com/sellers/pandas-ai) - **Year Founded:** 2023 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/pandasai/ (6 employees on LinkedIn®) ### 19. [Pandas Make](https://www.g2.com/products/pandas-make/reviews) Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool built on top of the Python programming language. It provides data structures and functions needed to work seamlessly with structured data, making it an essential library for data scientists and analysts. Key Features and Functionality: - Data Structures: Offers two primary data structures—Series (one-dimensional) and DataFrame (two-dimensional)—that handle a wide variety of data types. - Data Manipulation: Supports operations such as merging, reshaping, selecting, and data cleaning, enabling efficient data manipulation. - Data Analysis: Provides tools for performing statistical analysis, including descriptive statistics and aggregations. - Data Visualization: Integrates with libraries like Matplotlib and Seaborn to create informative visualizations. - Input/Output Operations: Facilitates reading from and writing to various file formats, including CSV, Excel, SQL databases, and more. Primary Value and User Solutions: Pandas simplifies the process of data analysis by offering intuitive and high-level data structures and methods. It addresses common challenges in data manipulation, such as handling missing data, aligning data from different sources, and performing complex transformations. By providing a consistent and efficient framework, Pandas enables users to focus on deriving insights from data rather than dealing with the intricacies of data processing. **Who Is the Company Behind Pandas Make?** - **Seller:** [Pandas Make](https://www.g2.com/sellers/pandas-make-01688dee-53ea-4bfd-a8d7-dcf3366f5b32) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 20. [Parabrain](https://www.g2.com/products/parabrain/reviews) Parabrain is an advanced artificial intelligence platform designed to enhance decision-making processes across various industries. By leveraging cutting-edge machine learning algorithms and data analytics, Parabrain provides users with actionable insights, predictive analytics, and automated solutions to complex problems. Its intuitive interface ensures seamless integration into existing workflows, enabling organizations to harness the power of AI without extensive technical expertise. Key Features and Functionality: - Predictive Analytics: Utilizes historical data to forecast future trends and outcomes, aiding in proactive decision-making. - Automated Decision Support: Offers real-time recommendations and solutions, reducing manual intervention and increasing efficiency. - Data Integration: Seamlessly connects with various data sources, ensuring comprehensive analysis and insights. - Customizable Models: Allows users to tailor AI models to specific industry needs and challenges. - User-Friendly Interface: Designed for ease of use, enabling quick adoption and minimal learning curve. Primary Value and Solutions: Parabrain addresses the challenge of data-driven decision-making by providing organizations with tools to analyze vast amounts of information efficiently. It empowers users to make informed decisions, optimize operations, and identify new opportunities, ultimately driving growth and competitive advantage. By automating complex processes and offering predictive insights, Parabrain reduces the burden on human resources and minimizes the risk of errors, leading to improved outcomes and increased productivity. **Who Is the Company Behind Parabrain?** - **Seller:** [Parabrain](https://www.g2.com/sellers/parabrain) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 21. [ParadeDB](https://www.g2.com/products/paradedb/reviews) ParadeDB is a modern alternative to Elasticsearch, designed as a PostgreSQL extension to enhance Postgres with advanced search and analytics capabilities. It enables real-time, full-text, semantic, and hybrid search directly within Postgres, eliminating the need for external search engines and complex ETL processes. By integrating seamlessly with existing Postgres deployments, ParadeDB simplifies data management and ensures data consistency without additional infrastructure overhead. Key Features and Functionality: - Full-Text Search with BM25 Scoring: Implements the BM25 algorithm for relevance ranking, supporting boolean, fuzzy, boosted, and keyword queries. - Hybrid Search: Combines semantic relevance (vector search) with full-text relevance for improved search accuracy. - Faceted Search: Facilitates easy bucketing and metric collection over search results, enhancing analytical capabilities. - Advanced Tokenization: Offers over 12 different tokenizers to process text into searchable tokens, with support for more than 20 languages, including dictionary-based tokenizers. - Real-Time Search: Ensures that text indexes and vector columns are automatically synchronized with underlying data, providing up-to-date search results. - Zero ETL Integration: Operates as a logical replica of any managed Postgres or can be installed within self-hosted Postgres, eliminating the need for data duplication and complex ETL pipelines. Primary Value and User Solutions: ParadeDB addresses the challenges associated with integrating external search engines like Elasticsearch, such as operational complexity, data duplication, and consistency issues. By embedding powerful search and analytics functionalities directly into Postgres, it provides a unified, efficient, and scalable solution for developers seeking to enhance their applications with advanced search capabilities without the burden of managing separate search infrastructures. **Who Is the Company Behind ParadeDB?** - **Seller:** [ParadeDB](https://www.g2.com/sellers/paradedb) - **Year Founded:** 2023 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/paradedb (3,827 employees on LinkedIn®) ### 22. [Paradigm AI](https://www.g2.com/products/paradigm-paradigm-ai/reviews) Paradigm is an AI-native workspace that revolutionizes data management by automating manual research and spreadsheet tasks. With a familiar spreadsheet interface, it enables users to gather, structure, and act on data with human-level precision. By integrating AI capabilities, Paradigm streamlines workflows, enhances data accuracy, and significantly reduces the time spent on data-related tasks. Key Features and Functionality: - Data Integration: Seamlessly upload data from various sources, including existing spreadsheets, CRMs, or APIs, and watch it instantly integrate into your workspace. - AI-Powered Enrichment: Enhance your data by adding columns and prompts that pull in relevant information from trusted sources, providing comprehensive insights. - Custom Columns: Define custom columns using AI to fetch specific data on demand, tailoring the workspace to your unique needs. - Collaborative Environment: Work with your team in real-time to track changes, leave comments, and enrich data together, fostering a collaborative and connected workspace. - Automated Actions: Create automations for data flow into and out of Paradigm, integrating seamlessly with your favorite third-party tools to streamline processes. Primary Value and Solutions: Paradigm addresses the inefficiencies of manual data collection and processing by automating these tasks, allowing users to focus on strategic decision-making. It provides a centralized platform where scattered datasets are unified, ensuring data consistency and accuracy. By leveraging AI, Paradigm offers actionable insights, reduces operational costs, and accelerates project timelines, making it an invaluable tool for professionals across various industries. **Who Is the Company Behind Paradigm AI?** - **Seller:** [Paradigm](https://www.g2.com/sellers/paradigm-ce04628c-0c53-456b-b8d9-16c12429c2e6) - **Year Founded:** 2023 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/tryparadigm (6 employees on LinkedIn®) ### 23. [Parsagon](https://www.g2.com/products/parsagon/reviews) Parsagon is a cutting-edge platform designed to streamline data extraction and analysis from various web sources. It empowers users to efficiently gather, process, and interpret web data, facilitating informed decision-making and strategic planning. Key Features and Functionality: - Automated Data Extraction: Parsagon automates the process of collecting data from diverse web pages, saving time and reducing manual effort. - Data Transformation: The platform offers tools to clean and transform raw data into structured formats suitable for analysis. - Integration Capabilities: Parsagon seamlessly integrates with various data analysis and visualization tools, enhancing workflow efficiency. - User-Friendly Interface: Designed with simplicity in mind, Parsagon provides an intuitive interface accessible to users with varying technical expertise. Primary Value and User Solutions: Parsagon addresses the challenge of efficiently extracting and utilizing web data by automating and simplifying the process. This enables businesses, researchers, and analysts to focus on deriving insights and making data-driven decisions without the complexities of manual data collection and processing. **Who Is the Company Behind Parsagon?** - **Seller:** [Parsagon](https://www.g2.com/sellers/parsagon) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/parsagon/ (2 employees on LinkedIn®) ### 24. [Parsewise](https://www.g2.com/products/parsewise/reviews) Parsewise is an advanced AI-powered platform designed to streamline data extraction and analysis from diverse sources, enabling businesses to make informed decisions efficiently. By automating the parsing of complex data structures, Parsewise reduces manual effort and minimizes errors, allowing organizations to focus on strategic initiatives. Key Features and Functionality: - Automated Data Extraction: Effortlessly extract data from various formats, including PDFs, spreadsheets, and web pages, without manual intervention. - Intelligent Parsing Algorithms: Utilize machine learning models to accurately interpret and structure unstructured data. - Customizable Workflows: Tailor data processing pipelines to meet specific business requirements and integrate seamlessly with existing systems. - Real-Time Processing: Process large volumes of data in real-time, ensuring timely insights and decision-making. - Scalability: Handle increasing data loads efficiently, accommodating business growth and evolving data needs. Primary Value and Solutions Provided: Parsewise addresses the challenge of managing and analyzing vast amounts of unstructured data by automating the extraction and parsing processes. This automation leads to significant time savings, reduced operational costs, and enhanced data accuracy. Businesses can leverage Parsewise to transform raw data into actionable insights, driving better outcomes and maintaining a competitive edge in their respective industries. **Who Is the Company Behind Parsewise?** - **Seller:** [Parsewise](https://www.g2.com/sellers/parsewise) - **Year Founded:** 2024 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/parsewise (919 employees on LinkedIn®) ### 25. [Pathora AI](https://www.g2.com/products/pathora-ai/reviews) Pathora AI is an advanced artificial intelligence platform designed to enhance business decision-making and operational efficiency. By leveraging cutting-edge machine learning algorithms, it provides predictive analytics and data-driven insights, enabling organizations to make informed choices and optimize their processes. Key Features and Functionality: - Predictive Analytics: Utilizes historical data to forecast future trends and outcomes, aiding in strategic planning. - Data Integration: Seamlessly combines data from various sources, ensuring comprehensive analysis. - Customizable Dashboards: Offers user-friendly interfaces tailored to specific business needs for real-time monitoring. - Automated Reporting: Generates detailed reports, reducing manual effort and enhancing accuracy. - Scalability: Adapts to businesses of all sizes, accommodating growth and evolving requirements. Primary Value and Solutions: Pathora AI addresses the challenge of data-driven decision-making by providing actionable insights derived from complex datasets. It empowers businesses to identify opportunities, mitigate risks, and streamline operations, ultimately leading to increased profitability and competitive advantage. **Who Is the Company Behind Pathora AI?** - **Seller:** [Pathora AI](https://www.g2.com/sellers/pathora-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 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) - [Machine Learning Software](https://www.g2.com/categories/machine-learning) - [Big Data Analytics Software](https://www.g2.com/categories/big-data-analytics) - [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) - [Generative AI Infrastructure Software](https://www.g2.com/categories/generative-ai-infrastructure) - [ Low-Code Machine Learning Platforms Software](https://www.g2.com/categories/low-code-machine-learning-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.