# Best Data Science and Machine Learning Platforms - Page 14

  *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:** 823

### Category Stats (May 2026)
- **Average Rating**: 4.45/5 (↑0.01 vs Apr 2026)
- **New Reviews This Quarter**: 171
- **Buyer Segments**: Mid-Market 40% │ Small-Business 35% │ Enterprise 25%
- **Top Trending Product**: Myriade (+0.5)
*Last updated: May 18, 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
- 13,200+ Authentic Reviews
- 823+ 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)

  
---

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ThoughtSpot is the Agentic Analytics Platform company for the enterprise. With natural language and AI, ThoughtSpot empowers everyone in an organization to ask data questions, get answers, and take action. Code-first for data teams and code-free for business users, ThoughtSpot is intuitive enough for anyone to use, yet built to handle large, complex cloud data at scale. Customers like Coca-Cola, Hilton Worldwide, and Capital One are unlocking the full potential of their data with ThoughtSpot.



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---

  ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026?
### 1. [Edgee](https://www.g2.com/products/edgee-edgee/reviews)
  Edgee is an advanced AI-powered platform designed to enhance business operations by automating complex processes and providing insightful analytics. By leveraging cutting-edge machine learning algorithms, Edgee enables organizations to streamline workflows, improve decision-making, and drive innovation across various industries. Key Features and Functionality: - Process Automation: Automates repetitive tasks, reducing manual effort and increasing operational efficiency. - Data Analytics: Offers comprehensive data analysis tools to uncover valuable insights and trends. - Customizable Solutions: Provides 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 operation. Primary Value and Solutions: Edgee addresses the challenge of managing complex business processes by offering a robust AI solution that automates tasks and delivers actionable insights. This leads to increased productivity, cost savings, and a competitive edge in the market. By implementing Edgee, businesses can focus on strategic initiatives while the platform handles routine operations, fostering innovation and growth.



**Who Is the Company Behind Edgee?**

- **Seller:** [Edgee](https://www.g2.com/sellers/edgee)
- **Year Founded:** 2024
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/edgee-ai (12 employees on LinkedIn®)



### 2. [eDNA Explorer](https://www.g2.com/products/edna-explorer/reviews)
  eDNA Explorer is an advanced bioinformatics platform that leverages environmental DNA (eDNA) to provide comprehensive insights into global biodiversity. By analyzing genetic material shed by organisms into their surroundings, eDNA Explorer enables users to detect and monitor species presence without direct observation, facilitating efficient and non-invasive ecosystem assessments. The platform is designed to serve a diverse range of users, including environmental research organizations, governmental bodies, and NGOs, by offering tools that simplify the collection, analysis, and sharing of eDNA data. Key Features and Functionality: - Comprehensive Project Support: eDNA Explorer offers end-to-end assistance, from project planning and sampling protocol development to laboratory analysis and data interpretation, ensuring seamless execution of eDNA-powered biodiversity projects. - User-Friendly Analysis Software: The platform provides an intuitive interface that transforms complex genetic and geospatial data into accessible visualizations, making it suitable for ecologists, land managers, and researchers alike. - Global Insight with Local Processing: By partnering with vetted local laboratories worldwide, eDNA Explorer ensures that samples are processed within their country of origin, maintaining data integrity and compliance with local regulations. - Data Sovereignty and Controlled Sharing: Users retain full ownership of their data, with options to keep it private, download it, or share it with collaborators or the public, providing flexibility and control over sensitive information. - Interactive Species Monitoring: The platform allows users to monitor target species and communities in various environments by creating baselines, tracking threatened and invasive species, and comparing eDNA findings with traditional biomonitoring methods. - Geospatial Analysis Tools: Integrated AI tools enable users to explore how environmental factors influence local species patterns, offering ranked lists of earth variables related to biodiversity data and facilitating site comparisons. - Advanced Analytics and Reporting: eDNA Explorer generates comprehensive reports and visualizations, tracks biodiversity trends over time, and provides publication-ready figures, aiding in informed decision-making and regulatory compliance. Primary Value and Solutions Provided: eDNA Explorer addresses the growing need for efficient, accurate, and non-invasive methods of biodiversity monitoring and ecosystem assessment. By harnessing the power of eDNA, geospatial data, and AI, the platform enables users to: - Enhance Conservation Efforts: By providing detailed insights into species presence and ecosystem health, eDNA Explorer supports the development and implementation of effective conservation strategies. - Streamline Regulatory Compliance: The platform aids organizations in meeting environmental assessment requirements by offering precise, standards-based reporting tools. - Inform Sustainable Practices: eDNA Explorer's data-driven insights assist industries such as agriculture, forestry, and fisheries in adopting sustainable practices that balance economic growth with environmental stewardship. - Facilitate Restoration Planning: By establishing baselines and monitoring changes over time, the platform supports restoration projects aimed at rehabilitating degraded ecosystems. In summary, eDNA Explorer serves as a critical tool for organizations seeking to understand and preserve biodiversity, offering a scalable and user-friendly solution for environmental DNA analysis and ecosystem management.



**Who Is the Company Behind eDNA Explorer?**

- **Seller:** [eDNA Explorer](https://www.g2.com/sellers/edna-explorer)
- **Year Founded:** 2023
- **HQ Location:** Mountain View, US
- **LinkedIn® Page:** https://www.linkedin.com/company/edna-explorer/ (9 employees on LinkedIn®)



### 3. [Electe](https://www.g2.com/products/electe-electe/reviews)
  Electe is an innovative platform designed to democratize access to artificial intelligence, enabling businesses of all sizes to make data-driven decisions. By transforming raw data into actionable insights, Electe empowers organizations to stay ahead in a competitive market. Its user-friendly interface ensures that even those without technical expertise can harness the power of AI to drive growth and efficiency. Key Features and Functionality: - Advanced AI-Powered Analytics: Electe offers sophisticated analytical tools that process vast amounts of data, providing clear and actionable insights to inform strategic decisions. - Customizable Reporting and Dashboards: Users can create tailored reports and interactive dashboards, facilitating real-time monitoring of key performance indicators and business metrics. - Scalable Solutions: With flexible pricing plans, Electe caters to businesses ranging from startups to large enterprises, ensuring scalability as organizations grow. - Seamless Integrations: The platform integrates effortlessly with existing tools and services, streamlining workflows and enhancing productivity. - Privacy by Design: Committed to data security, Electe incorporates privacy measures at every level, ensuring that sensitive information remains protected. Primary Value and Problem Solved: Electe addresses the challenge many businesses face in leveraging complex data for informed decision-making. By providing an accessible and powerful AI-driven platform, it eliminates the barriers to data analysis, allowing companies to uncover trends, optimize operations, and drive innovation. This empowerment leads to increased efficiency, reduced operational costs, and a significant competitive advantage in the marketplace.



**Who Is the Company Behind Electe?**

- **Seller:** [Electe](https://www.g2.com/sellers/electe-3ba72783-44e7-472e-844b-e48bedc6c323)
- **Year Founded:** 2023
- **HQ Location:** Milan, IT
- **LinkedIn® Page:** https://www.linkedin.com/company/electe/ (4 employees on LinkedIn®)



### 4. [Elevin AI](https://www.g2.com/products/elevin-ai/reviews)
  Elevin AI is an advanced artificial intelligence platform designed to enhance business operations by automating complex tasks and providing insightful analytics. It leverages cutting-edge machine learning algorithms to process vast amounts of data, enabling organizations to make informed decisions swiftly and accurately. By integrating seamlessly with existing systems, Elevin AI offers a scalable solution that adapts to various industry needs, driving efficiency and innovation. Key Features and Functionality: - Data Processing and Analysis: Elevin AI efficiently handles large datasets, extracting meaningful patterns and trends to inform strategic decisions. - Automation of Routine Tasks: The platform automates repetitive processes, reducing manual workload and minimizing human error. - Predictive Analytics: Utilizing advanced algorithms, Elevin AI forecasts future trends and outcomes, aiding in proactive planning. - Customizable Solutions: The system is adaptable to specific business requirements, ensuring a tailored approach to problem-solving. - Seamless Integration: Elevin AI integrates with existing software and platforms, facilitating a smooth transition and continuous workflow. Primary Value and User Solutions: Elevin AI addresses the challenge of managing and interpreting large volumes of data by providing a robust platform that automates analysis and delivers actionable insights. This empowers businesses to optimize operations, enhance productivity, and maintain a competitive edge in their respective markets. By reducing the reliance on manual processes and offering predictive capabilities, Elevin AI enables organizations to focus on strategic initiatives and innovation.



**Who Is the Company Behind Elevin AI?**

- **Seller:** [Elevin AI](https://www.g2.com/sellers/elevin-ai)
- **Year Founded:** 2024
- **HQ Location:** Hyderabad, IN
- **LinkedIn® Page:** https://www.linkedin.com/company/elevin-ai/ (1 employees on LinkedIn®)



### 5. [Emly Labs](https://www.g2.com/products/emly-labs/reviews)
  Emly Labs is a comprehensive no-code AI platform designed to empower individuals and organizations to build, deploy, and manage AI solutions without the need for programming expertise. By offering a suite of intuitive tools, Emly Labs enables users to harness the full potential of both generative and predictive AI, facilitating seamless integration into various business processes. Key Features and Functionality: - Emly Generative AI Bots: Create and deploy customizable AI-powered bots to enhance customer engagement and drive business growth. - Emly DataLab: Accelerate data preparation and enrichment through a no-code approach, ensuring scalability for handling large datasets. - Emly Hub: Manage and deploy AI projects seamlessly with integrated AI management and collaboration tools on a user-friendly platform. - Emly AutoML: Automatically preprocess data, select algorithms, tune parameters, and evaluate models with minimal human intervention. - Emly Vizard: Simplify data visualization with quick and intuitive tools to enhance storytelling and uncover insights. - Emly X-Data: Incorporate external data to enhance insights, accuracy, and generalization in AI models. Primary Value and Solutions Provided: Emly Labs democratizes AI by eliminating the need for coding skills, making AI accessible to a broader audience. It addresses common challenges in AI adoption, such as complex data preparation, model selection, and deployment, by providing an integrated, no-code environment. This approach accelerates time-to-market for AI solutions, reduces costs associated with development, and fosters a data-driven culture within organizations. By simplifying AI processes, Emly Labs enables businesses to focus on deriving actionable insights and achieving tangible outcomes from their AI initiatives.



**Who Is the Company Behind Emly Labs?**

- **Seller:** [Emlylabs](https://www.g2.com/sellers/emlylabs)
- **Year Founded:** 2023
- **HQ Location:** Bengaluru, IN
- **LinkedIn® Page:** https://www.linkedin.com/company/emlylabs/ (3 employees on LinkedIn®)



### 6. [Energent.ai](https://www.g2.com/products/energent-ai/reviews)
  Energent.ai is an AI-powered assistant designed to transform raw data into actionable insights without the need for coding or complex integrations. It automates repetitive tasks, enhances data processing, and provides real-time visualizations, enabling teams to focus on strategic decision-making. Key Features and Functionality: - Knowledge Hub: Aggregates data from multiple sources into a unified reference point, facilitating quick and efficient insight retrieval. - Customized Visualization: Generates clear, real-time dashboards and graphs, converting raw data into actionable intelligence without manual effort. - Agentic Workflow: Automates repetitive tasks such as data entry, scheduling, and form filling, freeing up human resources for higher-value work. - Data Engineering: Transforms unstructured information into structured, reliable datasets ready for analysis. - Continuous Learning: Learns from daily operations and historical data to improve recommendations over time. Primary Value and User Solutions: Energent.ai addresses the challenge of managing and interpreting vast amounts of data by providing an intuitive, no-code platform that automates data processing and analysis. It empowers users to make informed decisions quickly, reduces manual errors, and enhances productivity across various departments, including HR, finance, and operations. By simplifying complex workflows and offering real-time insights, Energent.ai enables organizations to focus on strategic initiatives and drive business growth.



**Who Is the Company Behind Energent.ai?**

- **Seller:** [Energent.ai](https://www.g2.com/sellers/energent-ai)
- **Year Founded:** 2023
- **HQ Location:** Abu Dhabi, AE
- **LinkedIn® Page:** https://www.linkedin.com/company/energent-ai/ (5 employees on LinkedIn®)



### 7. [Enterix AI](https://www.g2.com/products/enterix-ai/reviews)
  Enterix AI helps businesses transform their expertise, services, and intellectual property into scalable AI-powered enterprise software products. We provide a fully done-for-you solution, combining strategy, development, and deployment to eliminate the technical barriers that typically slow down innovation. What sets Enterix AI apart is our proprietary, bespoke software ecosystem, built in-house to accelerate development and deliver highly customised AI solutions tailored to each client’s business model. This allows us to move faster, integrate more deeply, and create systems that are both powerful and practical for real-world use. Beyond building software, we partner closely with our clients through one-on-one mentorship and hands-on support. Our expert team guides you through every stage, from concept and product design to implementation and growth. We also support go-to-market execution, helping you apply proven growth marketing strategies to attract customers and scale effectively. Whether you’re an agency, consultant, or business owner looking to productise your knowledge, Enterix AI provides the tools, systems, and expertise to turn your business into a scalable AI-driven operation. Our focus is on delivering solutions that generate real revenue, improve efficiency, and create long-term competitive advantage.



**Who Is the Company Behind Enterix AI?**

- **Seller:** [Enterix AI](https://www.g2.com/sellers/enterix-ai)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)



### 8. [EonLabs](https://www.g2.com/products/eonlabs/reviews)
  EonLabs is an AI-driven platform designed to simplify decision-making for businesses by transforming complex data into actionable insights. Recognizing that traditional data analytics often fall short in delivering tangible outcomes, EonLabs addresses the gap between data collection and effective decision execution. Key Features and Functionality: - Uncover Hidden Insights: Utilizes advanced analytics to reveal patterns and opportunities that conventional dashboards may overlook. - Prioritize Actionable Data: Filters out extraneous information, focusing on insights that align with business objectives. - Provide Actionable Recommendations: Offers clear next steps for each insight, facilitating swift transitions from analysis to implementation. - Enable Autonomous Decision-Making: Evolves to automate routine decisions over time, allowing teams to concentrate on strategic initiatives. Primary Value and Solutions Offered: EonLabs empowers businesses to fully leverage their data by bridging the gap between information and action. By delivering smarter insights and faster decision-making capabilities, the platform drives measurable outcomes such as revenue growth, cost reduction, and enhanced operational efficiency. Its real-time adaptability ensures that businesses remain agile, while the vision for autonomous execution aims to save time and reduce human error in repetitive decision processes.



**Who Is the Company Behind EonLabs?**

- **Seller:** [EonLabs](https://www.g2.com/sellers/eonlabs)
- **HQ Location:** Singapore
- **LinkedIn® Page:** https://www.linkedin.com/company/eonlabsai/ (1 employees on LinkedIn®)



### 9. [Esai](https://www.g2.com/products/esai/reviews)
  Esai is an advanced AI-powered platform designed to revolutionize the way businesses analyze and interpret data. By leveraging cutting-edge machine learning algorithms, Esai enables organizations to extract meaningful insights from complex datasets, facilitating informed decision-making and strategic planning. Key Features and Functionality: - Data Integration: Seamlessly combines data from multiple sources, providing a unified view for comprehensive analysis. - Predictive Analytics: Utilizes sophisticated algorithms to forecast trends and outcomes, aiding in proactive business strategies. - Customizable Dashboards: Offers intuitive dashboards that can be tailored to specific business needs, ensuring relevant metrics are easily accessible. - Automated Reporting: Generates detailed reports automatically, saving time and reducing the potential for human error. - Scalability: Designed to handle large volumes of data, making it suitable for businesses of all sizes. Primary Value and Solutions Provided: Esai addresses the challenge of data overload by transforming raw information into actionable insights. It empowers users to make data-driven decisions with confidence, enhances operational efficiency through automation, and provides a competitive edge by identifying emerging trends and opportunities. By simplifying complex data analysis processes, Esai enables businesses to focus on growth and innovation.



**Who Is the Company Behind Esai?**

- **Seller:** [ES.AI](https://www.g2.com/sellers/es-ai)
- **HQ Location:** New York, US
- **LinkedIn® Page:** https://www.linkedin.com/company/esaitoolkit (22 employees on LinkedIn®)



### 10. [Eventual](https://www.g2.com/products/eventual/reviews)
  Eventual is a data platform that empowers data scientists and engineers to build resilient data applications across various domains, including ETL, analytics, and machine learning. Its flagship product, Daft, is an open-source distributed data engine capable of running on an extensive scale, utilizing over 800,000 CPU cores daily. Eventual addresses the evolving needs of modern data workloads by bridging traditional data analytics with advanced ML/AI capabilities, allowing seamless execution of complex, multimodal data tasks. The company is well-funded by prominent investors and comprised of a team with robust backgrounds in high-performance computing and cloud infrastructure, all committed to developing cutting-edge data technologies. Eventual fosters a culture of intellectual curiosity and collaborative problem-solving, making it an engaging workplace for those passionate about the future of data. Key Features and Functionality: - Daft Data Engine: An open-source distributed data engine designed for large-scale data processing, capable of utilizing over 800,000 CPU cores daily. - Multimodal Data Processing: Supports complex, multimodal data tasks, bridging traditional data analytics with advanced ML/AI capabilities. - Python-Native Platform: Provides a Python-native environment that integrates seamlessly with existing tools, enhancing user experience for data scientists and engineers. - Cloud Integration: Integrates with popular cloud data storage services such as S3, PostgreSQL, and Snowflake, eliminating the need for complex data I/O or serialization code. - Scalability: Offers a scalable and open-source solution suitable for organizations of all sizes, from startups to large enterprises. Primary Value and Problem Solved: Eventual simplifies modern data workloads by providing a robust platform that integrates data engineering, machine learning, and analytics. By offering a Python-native environment and seamless cloud integration, it reduces the complexity of managing infrastructure, allowing data professionals to focus on developing and deploying data applications efficiently. This approach addresses the challenges of processing complex, unstructured data at scale, unlocking the potential of the remaining 80% of the world's data that is largely unstructured and made up of images and video.



**Who Is the Company Behind Eventual?**

- **Seller:** [Eventual](https://www.g2.com/sellers/eventual)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/showcase/daftengine/ (1 employees on LinkedIn®)



### 11. [Everstring AI](https://www.g2.com/products/everstring-ai/reviews)
  EverString AI is a cloud-based predictive analytics platform designed to empower B2B sales and marketing teams by leveraging artificial intelligence and machine learning. It enables users to identify and engage with high-quality customer prospects, optimize lead scoring, and enhance demand generation efforts. By analyzing vast datasets, EverString helps businesses build new pipelines and increase conversion rates effectively. Key Features and Functionality: - Predictive Scoring: Utilizes AI to assess and prioritize leads based on their likelihood to convert, allowing sales teams to focus on the most promising prospects. - Predictive Demand Generation: Identifies new, high-quality leads that align with a company's ideal customer profile, expanding the potential customer base. - Predictive Ad Targeting: Enhances marketing campaigns by targeting advertisements to prospects with the highest conversion potential, improving ROI. - AI-Powered Data Intelligence: Combines internal CRM data with over 20,000 external signals to provide comprehensive insights into market opportunities. - Seamless Integrations: Integrates with platforms like Microsoft Dynamics CRM Online, enabling users to optimize the prospect-to-customer journey within their existing workflows. Primary Value and User Solutions: EverString AI addresses the challenge of efficiently identifying and engaging with the most promising B2B prospects. By harnessing advanced data science and AI, it streamlines the lead generation process, enhances lead quality, and increases conversion rates. This empowers sales and marketing teams to focus their efforts on high-value accounts, ultimately driving revenue growth and improving marketing efficiency.



**Who Is the Company Behind Everstring AI?**

- **Seller:** [Chorus](https://www.g2.com/sellers/chorus)
- **HQ Location:** Vancouver, Washington, United States
- **LinkedIn® Page:** https://www.linkedin.com/company/zoominfo (4,333 employees on LinkedIn®)



### 12. [EvoML](https://www.g2.com/products/evoml/reviews)
  evoML is an AI Optimisation platform that enables businesses to drive greater business value with smart and efficient AI. Powered by TurinTech’s award winning research, evoML upskills tech and business teams to build, deploy and optimise AI models, at scale and at speed. By providing an end-to-end simplified platform, evoML enables users to easily access and understand data from across the organisation, and have it AI ready within a few clicks; improve business performance with better models (accurate, fast and energy-efficient); make confident business decisions with full transparency and automated explainable report. evoML helps businesses maximise return on investment form AI more effectively and efficiently.



**Who Is the Company Behind EvoML?**

- **Seller:** [TurinTech AI](https://www.g2.com/sellers/turintech-ai)
- **Year Founded:** 2018
- **HQ Location:** London, GB
- **LinkedIn® Page:** https://www.linkedin.com/company/turintechai (44 employees on LinkedIn®)



### 13. [Exegy](https://www.g2.com/products/exegy/reviews)
  Exegy is a leading provider of high-performance market data solutions, trading platforms, and execution technologies tailored for the capital markets. Leveraging over 20 years of innovation and a portfolio of more than 150 patents, Exegy delivers resilient, low-latency solutions that cater to the diverse needs of financial institutions, including market makers, proprietary traders, asset managers, and exchanges. Their offerings encompass real-time market data feeds, predictive trading signals, and comprehensive trading platforms, all designed to enhance trading performance and operational efficiency. Key Features and Functionality: - Real-Time Market Data: Exegy's solutions provide normalized, enriched, and filtered market data from over 300 global markets, covering asset classes such as equities, options, fixed income, commodities, and currencies. - Predictive Trading Signals: The Signum suite offers AI-driven real-time signals that predict price movements and identify hidden liquidity, enabling traders to make informed decisions with up to 80% accuracy. - Trading Platforms: Exegy's Metro platform supports automated, algorithmic, and click trading strategies across major US and European futures and options markets, providing a robust environment for executing complex trading strategies. - Managed Services: Exegy offers fully managed services, including 24/7 monitoring, capacity management, and support for exchange-driven changes, allowing clients to focus on their core trading activities while ensuring system reliability and compliance. Primary Value and Solutions Provided: Exegy's comprehensive suite of solutions addresses critical challenges in the financial trading landscape by delivering: - Enhanced Trading Performance: By providing low-latency, high-quality market data and predictive analytics, Exegy empowers traders to execute strategies more effectively, capturing opportunities with greater precision. - Operational Efficiency: The managed services model reduces the burden of infrastructure management, allowing firms to allocate resources towards strategic initiatives and innovation. - Scalability and Flexibility: Exegy's solutions are designed to scale with clients' needs, supporting growth and adaptation to evolving market conditions without compromising speed or stability. - Risk Mitigation: With real-time monitoring and support, Exegy ensures system resilience and compliance, mitigating operational risks associated with market data and trading infrastructure. By integrating advanced technology with expert services, Exegy enables financial institutions to navigate the complexities of modern trading environments, optimize performance, and achieve sustainable growth.



**Who Is the Company Behind Exegy?**

- **Seller:** [Exegy](https://www.g2.com/sellers/exegy-8a3519d9-01f8-45c9-aca2-ebdccf999fd1)
- **Year Founded:** 2003
- **HQ Location:** St Louis, Missouri, United States
- **LinkedIn® Page:** https://www.linkedin.com/company/exegy (394 employees on LinkedIn®)



### 14. [Exonic](https://www.g2.com/products/exonic/reviews)
  Exonic is an advanced AI-driven platform designed to revolutionize the way businesses manage and analyze their data. By leveraging cutting-edge machine learning algorithms, Exonic enables organizations to extract meaningful insights, automate complex processes, and enhance decision-making capabilities. Its intuitive interface and robust analytics tools cater to a wide range of industries, ensuring scalability and adaptability to various business needs. Key Features and Functionality: - Data Integration: Seamlessly connects with multiple data sources, allowing for comprehensive data aggregation and management. - Advanced Analytics: Utilizes sophisticated algorithms to perform predictive analytics, trend analysis, and anomaly detection. - Automation: Automates routine tasks and workflows, increasing operational efficiency and reducing human error. - Customizable Dashboards: Offers interactive and customizable dashboards for real-time data visualization and reporting. - Scalability: Designed to handle large datasets and scale with the growth of the organization. Primary Value and Solutions Provided: Exonic addresses the challenge of data overload by providing a centralized platform that simplifies data analysis and interpretation. It empowers users to make informed decisions quickly, enhances productivity through automation, and drives business growth by uncovering actionable insights. By transforming raw data into strategic assets, Exonic enables organizations to stay competitive in a data-driven world.



**Who Is the Company Behind Exonic?**

- **Seller:** [Exonic](https://www.g2.com/sellers/exonic)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)



### 15. [Expert.ai](https://www.g2.com/products/expert-ai/reviews)
  Expert.ai Studio is a fully integrated, low-code development environment for building and deploying custom AI-based text models to address any linguistic challenge. Our solution helps organizations and developers to create advanced and unique solutions to extend the scope of intelligent process automation and make knowledge discovery more effective. Expert.ai Studio applies natural language understanding (NLU) capabilities and a fine-grained text processing configuration to achieve precise comprehension of your content. As a result, you gain complete control over your data so you can use it more efficiently and at scale in support of your business operations.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1

**Who Is the Company Behind Expert.ai?**

- **Seller:** [Expert.ai](https://www.g2.com/sellers/expert-ai)
- **HQ Location:** Modena, IT
- **LinkedIn® Page:** https://www.linkedin.com/company/expert-ai/ (266 employees on LinkedIn®)
- **Ownership:** BIT:EXSY
- **Total Revenue (USD mm):** $31

**Who Uses This Product?**
  - **Company Size:** 100% Enterprise


### 16. [Exploratory](https://www.g2.com/products/exploratory/reviews)
  Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 3
**How Do G2 Users Rate Exploratory?**

- **Ease of Admin:** 9.2/10 (Category avg: 8.5/10)

**Who Is the Company Behind Exploratory?**

- **Seller:** [Exploratory](https://www.g2.com/sellers/exploratory)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


#### What Are Exploratory's Pros and Cons?

**Pros:**

- ML Modeling (1 reviews)
- Problem Solving (1 reviews)
- Productivity Improvement (1 reviews)


### 17. [Extracta.ai](https://www.g2.com/products/extracta-ai/reviews)
  Extracta.ai is an advanced AI-driven platform that automates the extraction of structured data from unstructured documents, such as invoices, resumes, contracts, and receipts. By leveraging cutting-edge Optical Character Recognition (OCR) technology and fine-tuned Large Language Models (LLMs), Extracta.ai achieves up to 99% accuracy in data extraction without the need for prior training. This solution is designed to streamline workflows, reduce manual data entry, and enhance operational efficiency across various industries, including finance, healthcare, and logistics. Key Features and Functionality: - Versatile Document Processing: Supports a wide range of document formats, including PDFs, Word documents, text files, and scanned images (PNG, JPG), enabling comprehensive data extraction capabilities. - Customizable Extraction: Allows users to define their own templates and tailor the data extraction process to meet specific requirements, enhancing the relevance and accuracy of the extracted data. - High Accuracy: Utilizes advanced algorithms to ensure up to 99% accuracy in data extraction, minimizing errors and improving data reliability. - Ease of Integration: Offers a RESTful API for seamless integration into existing systems and workflows, facilitating automated data processing without significant infrastructure changes. - Data Privacy and Security: Prioritizes the confidentiality and integrity of user data by adhering to the highest data protection standards, including full compliance with GDPR regulations. Primary Value and Problem Solved: Extracta.ai addresses the challenge of efficiently processing large volumes of unstructured documents by automating data extraction tasks. This automation reduces the reliance on manual data entry, thereby decreasing the likelihood of errors and increasing overall productivity. By transforming unstructured data into structured, actionable information, Extracta.ai empowers businesses to make informed decisions more quickly and effectively. Its user-defined templates and no pre-training requirement offer unparalleled flexibility, making it an ideal solution for organizations seeking to enhance their data processing capabilities without extensive setup or training.



**Who Is the Company Behind Extracta.ai?**

- **Seller:** [Extracta.ai](https://www.g2.com/sellers/extracta-ai)
- **Year Founded:** 2023
- **HQ Location:** Bucharest, RO
- **LinkedIn® Page:** https://www.linkedin.com/company/extracta-ai/ (2 employees on LinkedIn®)



### 18. [Extractninja](https://www.g2.com/products/extractninja/reviews)
  ExtractNinja is a powerful data extraction tool designed to simplify the process of retrieving and organizing information from various online sources. It enables users to efficiently gather data without the need for complex coding or manual effort, making it an ideal solution for businesses and individuals seeking to automate their data collection processes. Key Features and Functionality: - Automated Data Extraction: ExtractNinja automates the process of extracting data from websites, reducing the time and effort required for manual data collection. - User-Friendly Interface: The platform offers an intuitive interface that allows users to set up and manage extraction tasks with ease, regardless of their technical expertise. - Customizable Extraction Parameters: Users can define specific parameters and criteria to tailor the data extraction process to their unique needs. - Data Organization and Export: Extracted data is organized systematically and can be exported in various formats, facilitating seamless integration with other tools and systems. Primary Value and Problem Solved: ExtractNinja addresses the challenge of efficiently collecting and managing large volumes of data from the web. By automating the extraction process, it eliminates the need for manual data gathering, reducing errors and saving valuable time. This empowers users to focus on analyzing and utilizing the data to drive informed decisions and strategies.



**Who Is the Company Behind Extractninja?**

- **Seller:** [ExtractNinja](https://www.g2.com/sellers/extractninja)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)



### 19. [Factful](https://www.g2.com/products/factful/reviews)
  Factful is a comprehensive data management platform designed to streamline the collection, analysis, and visualization of complex datasets. It empowers organizations to make informed decisions by providing real-time insights and facilitating data-driven strategies. Key Features and Functionality: - Data Integration: Seamlessly connects with various data sources, ensuring a unified view of information. - Advanced Analytics: Offers robust analytical tools to uncover patterns and trends within datasets. - Customizable Dashboards: Provides interactive dashboards tailored to specific business needs. - Collaboration Tools: Enables teams to share insights and work together effectively. - Security Measures: Implements stringent security protocols to protect sensitive data. Primary Value and User Solutions: Factful addresses the challenge of managing and interpreting large volumes of data by offering an intuitive platform that simplifies these processes. It enhances operational efficiency, supports strategic planning, and drives business growth by delivering actionable insights. Users benefit from reduced time spent on data processing and increased accuracy in their analyses, leading to more informed decision-making.



**Who Is the Company Behind Factful?**

- **Seller:** [Factful](https://www.g2.com/sellers/factful)
- **Year Founded:** 2023
- **HQ Location:** Oakville, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/factfulpage/ (8 employees on LinkedIn®)



### 20. [Faculty.ai](https://www.g2.com/products/faculty-ai/reviews)
  We help our customers make better decisions. Whether you are looking to grow, work more efficiently, or deliver higher quality services, we build industry-leading AI that helps you turn your data into deeper insights, better strategies and smarter decisions.



**Who Is the Company Behind Faculty.ai?**

- **Seller:** [faculty platform](https://www.g2.com/sellers/faculty-platform)
- **Year Founded:** 2014
- **HQ Location:** London, England, United Kingdom
- **LinkedIn® Page:** https://www.linkedin.com/company/facultyai (921 employees on LinkedIn®)



### 21. [FalkorDB](https://www.g2.com/products/falkordb/reviews)
  An ultra-low latency Graph Database that perfects the Knowledge Graph for GraphRAG. Effectively overcoming the existing limitations of RAG for GenAI & Large Language Models (LLM).


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 4
**How Do G2 Users Rate FalkorDB?**

- **Ease of Admin:** 8.9/10 (Category avg: 8.5/10)

**Who Is the Company Behind FalkorDB?**

- **Seller:** [FalkorDB](https://www.g2.com/sellers/falkordb)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 22. [Fast](https://www.g2.com/products/fast-ai-fast/reviews)
  Fast.ai is a non-profit research group dedicated to democratizing deep learning and artificial intelligence by making these technologies more accessible to individuals from diverse backgrounds. Founded in 2016 by Jeremy Howard and Rachel Thomas, Fast.ai offers free online courses, an open-source deep learning library built on PyTorch, and cutting-edge research to simplify the implementation of AI models. Their mission is to enable people, regardless of their prior experience, to harness the power of deep learning in practical applications. Key Features and Functionality: - Free Online Courses: Fast.ai provides comprehensive courses like "Practical Deep Learning for Coders," designed to teach deep learning through hands-on coding and real-world applications. - Fastai Library: An open-source deep learning library that offers high-level components for building state-of-the-art models in various domains, including computer vision, natural language processing, and tabular data analysis. - Top-Down Teaching Approach: Emphasizes starting with practical applications and working code before delving into underlying theories, making learning more intuitive and effective. - Community Support: A vibrant community that fosters collaboration, providing forums and resources for learners to share knowledge and seek assistance. Primary Value and Problem Solved: Fast.ai addresses the challenge of making deep learning accessible to a broader audience by removing barriers such as the need for advanced mathematical knowledge or extensive coding experience. By offering free, practical courses and user-friendly tools, Fast.ai empowers individuals to apply deep learning techniques to real-world problems, thereby fostering innovation and inclusivity in the AI field.



**Who Is the Company Behind Fast?**

- **Seller:** [Fast AI](https://www.g2.com/sellers/fast-ai-ad3618ee-c234-4a71-ba78-223da6fe8f04)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)



### 23. [Featurewave](https://www.g2.com/products/featurewave-featurewave/reviews)
  Featurewave is an advanced AI-driven platform designed to enhance product development by providing real-time insights and analytics. It enables teams to make data-driven decisions, streamline workflows, and accelerate time-to-market. Key Features and Functionality: - Real-Time Analytics: Offers immediate insights into product performance and user engagement. - Collaborative Tools: Facilitates seamless communication and collaboration among team members. - Customizable Dashboards: Allows users to tailor dashboards to specific project needs. - Integration Capabilities: Easily integrates with existing tools and platforms. - Predictive Modeling: Utilizes AI to forecast trends and potential challenges. Primary Value and Solutions: Featurewave addresses the common challenges in product development by providing actionable insights and fostering collaboration. It helps teams reduce development cycles, improve product quality, and respond swiftly to market demands, ultimately leading to increased customer satisfaction and business growth.



**Who Is the Company Behind Featurewave?**

- **Seller:** [Featurewave](https://www.g2.com/sellers/featurewave)
- **Year Founded:** 2023
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/amplify10/ (4 employees on LinkedIn®)



### 24. [Fencer](https://www.g2.com/products/fencer/reviews)
  Fencer is a comprehensive platform designed to streamline the management of software development projects by integrating various tools and services into a unified interface. It offers a centralized hub where teams can collaborate, track progress, and manage tasks efficiently, enhancing productivity and reducing the complexity associated with juggling multiple platforms. Key Features and Functionality: - Unified Dashboard: Provides a single view of all project activities, enabling teams to monitor progress and identify bottlenecks promptly. - Task Management: Facilitates the creation, assignment, and tracking of tasks, ensuring clarity and accountability within the team. - Integration Capabilities: Seamlessly connects with popular development tools and services, allowing for a cohesive workflow without the need to switch between applications. - Collaboration Tools: Offers communication channels and document sharing features to enhance team collaboration and information sharing. - Analytics and Reporting: Delivers insightful reports and analytics to help teams make data-driven decisions and improve project outcomes. Primary Value and User Solutions: Fencer addresses the common challenges faced by software development teams, such as fragmented workflows and communication gaps, by providing an all-in-one platform that consolidates essential tools and services. This integration leads to improved efficiency, better project visibility, and enhanced team collaboration, ultimately resulting in faster delivery times and higher-quality software products.



**Who Is the Company Behind Fencer?**

- **Seller:** [Fencer](https://www.g2.com/sellers/fencer)
- **Year Founded:** 2024
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/fencer-security (12 employees on LinkedIn®)



### 25. [Fetch Hive](https://www.g2.com/products/fetch-hive/reviews)
  Fetch Hive is an all-in-one Generative AI Collaboration Platform packed with features and tools that save you time and increase productivity: Generative AI Prompt Management: The platform helps in building and managing AI prompts, enabling users to refine and achieve desired outputs efficiently. AI Workflows: Integrate the latest AI tools such as prompting and Agents, alongside your existing tools like Google Search, Website Scraping and more, to create quality, relatable content. Custom RAG Chat Agents: Users can create chat agents with retrieval-augmented generation, which improves response quality and relevance. Centralized Data Storage: It provides a system for easily accessing and managing all necessary data for AI model training and deployment. Real-Time Data Integration: By incorporating real-time data from Google Search, Fetch Hive enhances workflows with up-to-date information, boosting decision-making and productivity. Fetch Hive is a comprehensive solution for those looking to develop and manage generative AI projects effectively, optimizing interactions with advanced features and streamlined workflows.



**Who Is the Company Behind Fetch Hive?**

- **Seller:** [Fetch Hive](https://www.g2.com/sellers/fetch-hive)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/fetchhive (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.