# Best Data Science and Machine Learning Platforms - Page 5 *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 - 13,100+ 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) --- **Sponsored** ### Kpow for Apache Kafka® Kpow is a sophisticated enterprise Kafka management tool designed to enhance the experience of engineering teams by providing a comprehensive solution for managing, monitoring, exploring, and securing Kafka environments. This JVM-based web application serves as an all-in-one console, empowering Kafka engineers with the capabilities they need to streamline their operations and improve productivity. Targeted primarily at engineering teams working with Kafka, Kpow addresses the complexities of managing multiple Kafka clusters, schema registries, and connection installations. With Kpow, users can efficiently monitor and control their Kafka resources from a single interface, simplifying the management process and reducing the time spent on routine tasks. The tool is particularly beneficial for organizations that rely heavily on Kafka for data streaming and processing, as it provides essential functionalities that enhance observability and operational efficiency. One of the standout features of Kpow is its real-time monitoring and visualization capabilities. Users can quickly identify unbalanced brokers and gain insights into how data is distributed across their Kafka Streams topologies. This level of visibility is crucial for diagnosing production issues and optimizing performance. Kpow's advanced search functionalities, including Data Inspect, Streaming Search, and kREPL, enable users to search through vast amounts of messages at remarkable speeds, allowing for rapid troubleshooting and data analysis. Kpow also prioritizes security and access control, making it suitable for enterprise environments. It integrates seamlessly with standard authentication providers and offers role-based access controls, ensuring that user actions can be finely tuned to meet organizational security requirements. Additional security features, such as data masking and audit logs, further enhance the tool's capability to operate in sensitive environments, including air-gapped installations. Installation of Kpow is straightforward, requiring only a single Docker container or JAR file, which operates efficiently with minimal resource requirements of 1GB memory and 1 CPU for production use. This ease of deployment, combined with its powerful features, positions Kpow as a valuable asset for organizations looking to maximize their Kafka infrastructure while maintaining robust security and operational control. [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=1509&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=1041&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=133071&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%3Fpage%3D13&secure%5Btoken%5D=1f788e7f44435ebe972108c7587b30cab8b8f8957d72ed9b930de732689b36e4&secure%5Burl%5D=http%3A%2F%2Ffactorhouse.io%2F&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Data Science and Machine Learning Platforms Products in 2026? ### 1. [Deci AI](https://www.g2.com/products/deci-ai/reviews) Advancements in AI, powered by deep learning, have triggered groundbreaking innovations. But, long development cycles, high compute costs, and poor inference performance are making it almost impossible for enterprises to productize AI. At Deci, we realized that the solution lay in harnessing the AI itself. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Deci AI?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) **Who Is the Company Behind Deci AI?** - **Seller:** [Deci AI](https://www.g2.com/sellers/deci-ai) - **Year Founded:** 2019 - **HQ Location:** Tel Aviv, IL - **LinkedIn® Page:** https://www.linkedin.com/company/deciai/?originalSubdomain=il (30 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market #### What Are Deci AI's Pros and Cons? **Pros:** - Ease of Use (1 reviews) - Easy Integrations (1 reviews) - Efficiency (1 reviews) - Fast Processing (1 reviews) - Innovation (1 reviews) **Cons:** - Complexity (1 reviews) - Difficult Setup (1 reviews) - Initial Difficulties (1 reviews) ### 2. [Deep.BI](https://www.g2.com/products/deep-bi/reviews) Deep.BI measures content consumption metrics and provides user engagement scoring to power publisher's content delivery, marketing tools and paywalls to grow, engage and retain audiences. Deep.BI collects all kinds of raw event data related to publishing, like reader’s behavior and content performance, and analyzes this data in real-time (sub-second latency between ingestion and data visualization). By collecting first-party raw data (no sampling & no aggregation), publishers get unprecedented flexibility in building their own metrics, reports, and different strategies for different kinds of content. This also allows publishers to quickly test hypotheses on both live and historical data. These dashboards and reports are shareable and customizable across teams making the workload on the analysts much lighter and gives them the ability to deliver what they want to deliver in the way they want and in lightning speeds! **Average Rating:** 4.4/5.0 **Total Reviews:** 10 **How Do G2 Users Rate Deep.BI?** - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Deep.BI?** - **Seller:** [Deep.BI](https://www.g2.com/sellers/deep-bi) - **Year Founded:** 2016 - **HQ Location:** San Francisco, California - **Twitter:** @_DeepBI (963 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/deep-bi/ (20 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 40% Mid-Market #### What Are Deep.BI's Pros and Cons? **Pros:** - Analytics (2 reviews) - Insights (2 reviews) - Insights Generation (2 reviews) - Audience Engagement (1 reviews) - Automation (1 reviews) **Cons:** - Coding Difficulty (1 reviews) - Confusing Interface (1 reviews) - Not Intuitive (1 reviews) - Poor Interface Design (1 reviews) - Poor UI Design (1 reviews) ### 3. [Deep Learning Containers](https://www.g2.com/products/deep-learning-containers/reviews) Google's Deep Learning Containers are pre-configured Docker images designed to streamline the development and deployment of deep learning models. These containers come equipped with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, along with their dependencies, enabling data scientists and developers to focus on model development without the hassle of environment setup. Key Features and Functionality: - Pre-configured Environments: Each container includes essential deep learning frameworks and libraries, ensuring compatibility and reducing setup time. - Scalability: Seamless integration with Google Cloud services allows for efficient scaling of training and inference tasks. - Flexibility: Support for various hardware accelerators, including GPUs and TPUs, enhances performance for computationally intensive tasks. - Portability: Consistent environments across development, testing, and production stages facilitate smoother transitions and deployments. Primary Value and Problem Solved: Deep Learning Containers address the complexities associated with setting up and managing deep learning environments. By providing ready-to-use, optimized containers, they eliminate the need for manual installation and configuration of machine learning frameworks and dependencies. This accelerates the development process, ensures consistency across different stages of model deployment, and allows teams to allocate more resources toward innovation and model refinement rather than infrastructure management. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Deep Learning Containers?** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 9.2/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 9.2/10 (Category avg: 8.2/10) - **Ease of Admin:** 6.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Deep Learning Containers?** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,915,529 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market #### What Are Deep Learning Containers's Pros and Cons? **Pros:** - Easy Integrations (1 reviews) - Integrated Platform (1 reviews) **Cons:** - Complexity (1 reviews) ### 4. [GGML](https://www.g2.com/products/ggml/reviews) GGML is a tensor library for machine learning, enabling complex models on regular hardware. **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate GGML?** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind GGML?** - **Seller:** [GGML](https://www.g2.com/sellers/ggml) - **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:** 50% Mid-Market, 50% Small-Business ### 5. [Gurobi Optimizer](https://www.g2.com/products/gurobi-optimizer/reviews) With the Gurobi Optimizer, you can identify provably optimal solutions to the world’s most complex problems—including linear, nonlinear, and quadratic problems—using any combination of continuous and integer variables. Our user-friendly functionalities include multiple objectives, multiple scenarios, solution pools, general constraints, infeasibility analysis, a partition heuristic, Python matrix API, and more—all backed by our 100% PhD-level expert support. Plus, Gurobi is always free for students, faculty, researchers, and even recent graduates. Founded in 2008, Gurobi has operations in the Americas, Europe, and Asia. It serves customers across 40+ industries, including organizations like SAP, Air France, and the National Football League. Discover the Gurobi difference at gurobi.com. **Average Rating:** 4.6/5.0 **Total Reviews:** 21 **How Do G2 Users Rate Gurobi Optimizer?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 0.0/10 (Category avg: 8.2/10) **Who Is the Company Behind Gurobi Optimizer?** - **Seller:** [Gurobi ](https://www.g2.com/sellers/gurobi) - **Year Founded:** 2008 - **HQ Location:** Beaverton, OR - **Twitter:** @gurobi (5,055 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/511132/ (206 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 52% Enterprise, 24% Mid-Market ### 6. [Iguazio](https://www.g2.com/products/iguazio/reviews) Iguazio’s AI software enables enterprises to develop, deploy and manage AI applications, drastically shortening the time required to create real business value with AI. Using Iguazio, organizations can develop AI models at scale and in real time, deploy them anywhere (multi-cloud, on-prem or edge), and bring to life their most ambitious AI-driven strategies. Enterprises spanning a wide range of verticals use Iguazio to solve the complexities of MLOps and create business impact through a multitude of ML and Generative AI use cases such as chatbot automation, fraud prediction, real-time recommendation engines and predictive maintenance. Iguazio was acquired by McKinsey & Company in January of 2023 and is now a part of QuantumBlack, McKinsey's AI arm. Iguazio brings data science to life. **Average Rating:** 4.8/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Iguazio?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 7.5/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Iguazio?** - **Seller:** [Iguazio](https://www.g2.com/sellers/iguazio) - **Year Founded:** 2014 - **HQ Location:** Herzliya, IL - **Twitter:** @iguazio (933 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/iguazio/ (74 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 25% Enterprise #### What Are Iguazio's Pros and Cons? **Pros:** - AI Capabilities (1 reviews) - AI Integration (1 reviews) - Automation (1 reviews) - Customization (1 reviews) - Deployment Ease (1 reviews) **Cons:** - Complexity (1 reviews) - Cost Transparency (1 reviews) - Lacking Features (1 reviews) - Lack of Guidance (1 reviews) - Learning Curve (1 reviews) ### 7. [Kili](https://www.g2.com/products/kili/reviews) Kili Technology is a collaborative AI data platform designed to assist enterprise customers in creating production-ready AI training data. Founded in Paris in 2018, Kili Technology caters to a diverse range of industries, including healthcare, financial services, manufacturing, defense, and technology. The platform is engineered to support teams of varying sizes, accommodating anywhere from 10 to over 500 concurrent users, and processes millions of assets annually. The core functionality of Kili Technology lies in its ability to facilitate collaboration among cross-functional teams. Unlike traditional labeling tools that primarily serve machine learning engineers, Kili connects data science teams with business stakeholders and subject matter experts. This integration enhances the AI development lifecycle by streamlining processes from annotation and labeling to validation and model feedback. As a result, users can ensure that the data used for training AI models is not only accurate but also relevant to the specific business context. Kili Technology is particularly beneficial for organizations looking to harness the power of AI while maintaining a high level of data quality. The platform supports various data modalities, allowing teams to work with text, images, audio, and video data seamlessly. This versatility makes it suitable for a wide range of applications, from developing natural language processing models to image recognition systems. By fostering collaboration among different roles within an organization, Kili enhances the overall efficiency of the AI development process. Key features of Kili Technology include an intuitive user interface that simplifies the labeling process, robust tools for data validation, and comprehensive feedback mechanisms that enable continuous improvement of AI models. Additionally, the platform offers advanced analytics capabilities, allowing teams to track progress and identify areas for enhancement. These features collectively empower organizations to build high-quality training datasets that meet the demands of complex AI applications. Kili Technology stands out in the competitive landscape of AI data platforms by prioritizing collaboration and usability. By bridging the gap between technical and non-technical stakeholders, it ensures that the development of AI solutions is a cohesive effort. This approach not only accelerates the time to market for AI initiatives but also enhances the overall quality of the training data, ultimately leading to more effective AI models. **Average Rating:** 4.7/5.0 **Total Reviews:** 52 **How Do G2 Users Rate Kili?** - **Ease of Admin:** 9.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Kili?** - **Seller:** [Kili Technology](https://www.g2.com/sellers/kili-technology) - **Company Website:** https://kili-technology.com - **Year Founded:** 2018 - **HQ Location:** Paris, FR - **Twitter:** @Kili_Technology (441 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/33266852 (49 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 38% Mid-Market, 34% Small-Business #### What Are Kili's Pros and Cons? **Pros:** - Data Labeling (1 reviews) - Data Labelling (1 reviews) - Ease of Use (1 reviews) - Model Variety (1 reviews) **Cons:** - Limited Features (1 reviews) - Missing Features (1 reviews) ### 8. [Kortical](https://www.g2.com/products/kortical/reviews) Kortical is an AI Cloud / ML platform that delivers acceleration without sacrificing any control over the model building process. Build AI in code or in the UI, depending on your preference. Key features are: - Powerful SDK and UI interfaces - Exploratory Data Analysis - Data Cleaning & Feature Engineering - Model Building With Interactive AutoML - Experiment Tracking - Explainability - "1 click" Deployment - Instant Apps to create Enterprise Grade ML apps in minutes (code included) The cloud infrastructure is set-up so all you need is a login and you can from raw data to fully deployed model in hours. Using Kortical's app templates Data scientists are empowered to own the process from raw data to fully deployed, enterprise grade application, based on industry leading stack, including Kubernetes, uwsgi and continuous integration. With Kortical you get the best of data scientist and machine and we have a team of data scientists on hand to support you at any stage of your ML solution delivery. Kortical gets more accurate model results than Google AutoML, Azure, Datarobot and more, do reach out to check it out. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Kortical?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 6.7/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Kortical?** - **Seller:** [Kortical](https://www.g2.com/sellers/kortical) - **Year Founded:** 2016 - **HQ Location:** London, GB - **Twitter:** @Kortical_ (352 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kortical/ (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market ### 9. [Observable](https://www.g2.com/products/observable/reviews) Observable is a data analysis and exploration platform that helps data analysts and their stakeholders understand their business data, build charts and other visualizations, and communicate insights. Built for teams, powered by AI, and backed by a global community of data practitioners, Observable offers a single workspace for data exploration, custom chart creation, and cross-functional data collaboration. Observable's collaborative whiteboard for data analysis, Observable Canvases, helps analysts with exploratory data analysis, advanced chart creation, and data storytelling. It makes the data analysis process more streamlined by bringing querying, chart creation, and sharing all in one platform. Users can choose between code, UI, or AI, and easily move between all three depending on the task at hand. The AI is also deeply integrated and turnkey, operating transparently on the canvas to allow users to understand, interpret, and fine-tune its results and reasoning, building trust in its recommendations. Additionally, whiteboarding features like comments, illustrations, and annotations help bring stakeholders into the data analysis process to reduce frustrating back-and-forths. Sophisticated data visualizations such as beeswarms, Sankey diagrams, choropleths, and more are available out-of-the-box to enable users to easily create and share expressive, interactive charts. Users can embed charts in internal apps, and generate polished, stakeholder-friendly dashboards in just a few clicks. **Average Rating:** 4.4/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Observable?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 6.7/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Observable?** - **Seller:** [Observable](https://www.g2.com/sellers/observable) - **Year Founded:** 2017 - **HQ Location:** San Francisco, US - **Twitter:** @observablehq (25,314 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/observable/ (34 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 25% Enterprise #### What Are Observable's Pros and Cons? **Pros:** - Charting Features (1 reviews) - Collaboration (1 reviews) - Data Visualization (1 reviews) - Design Aesthetics (1 reviews) - Ease of Use (1 reviews) **Cons:** - Data Management Issues (1 reviews) - Large Data Handling (1 reviews) - Slow Performance (1 reviews) ### 10. [Obviously AI](https://www.g2.com/products/obviously-ai/reviews) Obviously AI is the fastest and easiest automated machine learning software that enables anyone to build predictive AI models in minutes, without writing code. All you do is connect your historical data, click a couple of buttons and your predictive AI models will be ready to use in just a matter of minutes. You can share these predictions with your team, simulate what-if scenarios and use our APIs to integrate with your everyday apps & services and automatically take action in real time. **Average Rating:** 4.0/5.0 **Total Reviews:** 4 **Who Is the Company Behind Obviously AI?** - **Seller:** [Obviously AI](https://www.g2.com/sellers/obviously-ai) - **Year Founded:** 2025 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/zamshq (24 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Small-Business ### 11. [Plainsight](https://www.g2.com/products/plainsight/reviews) Plainsight is the leader in proven vision AI. Providing the unique combination of AI strategy, a vision AI platform, and deep learning expertise, Plainsight develops, implements, and oversees transformative computer vision solutions for enterprises. Through the widest breadth of managed services and a vision AI platform for centralized processes and standardized pipelines, Plainsight makes computer vision repeatable and accountable across all enterprise vision AI initiatives. Plainsight solves problems where others have failed and empowers businesses across industries to realize the full potential of their visual data with the lowest barriers to production, fastest value generation, and monitoring for long-term success. For more information, visit https://plainsight.ai. **Average Rating:** 4.5/5.0 **Total Reviews:** 5 **Who Is the Company Behind Plainsight?** - **Seller:** [Plainsight](https://www.g2.com/sellers/plainsight) - **Year Founded:** 2024 - **HQ Location:** Greater Seattle Area, US - **Twitter:** @PlainsightAI (1,457 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/plainsightai/ (22 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 80% Small-Business, 20% Mid-Market #### What Are Plainsight's Pros and Cons? **Pros:** - AI Capabilities (1 reviews) - AI Integration (1 reviews) - AI Modeling (1 reviews) - AI Technology (1 reviews) - Innovation (1 reviews) **Cons:** - Required Expertise (1 reviews) - Required Knowledge (1 reviews) ### 12. [Qualetics](https://www.g2.com/products/qualetics/reviews) Discover Qualetics, your gateway to effortless AI automation. Our cutting-edge platform is designed to provide businesses with a seamless experience with its intuitive no-code AI capabilities. Say goodbye to coding complexities and hello to the future of AI innovation! Key Features: No Code AI: Qualetics empowers you with a no-code AI platform, eliminating the need for programming expertise. Dive into the world of artificial intelligence effortlessly and bring your ideas to life without writing a single line of code. Pre-Trained AI Models: Benefit from over 25+ pre-trained AI models covering a diverse range of applications. Qualetics ensures that you have access to state-of-the-art models for tasks such as text analysis, image recognition, document processing, and audio/video data interpretation. Self Learning AI Models: Qualetics goes beyond static models. Our platform features self-learning AI models that adapt and evolve over time, ensuring that your solutions stay ahead of the curve and continuously improve their performance. Data Processing Capabilities: Seamlessly process text, image, document, audio, and video data with Qualetics. Our platform is your all-in-one solution for comprehensive data processing, enabling you to extract valuable insights from various types of content. Multi-Tenancy: Qualetics understands the importance of scalability and collaboration. Our multi-tenancy support allows multiple users or teams to work concurrently within the platform, ensuring efficiency and collaboration at every level. Security and Governance: Trust is paramount. Qualetics prioritizes your data's security with robust measures and governance protocols. Rest assured that your AI endeavors are protected and compliant with industry standards. Real-Time Observability: Stay informed and in control with real-time observability features. Monitor your AI models' performance, track usage metrics, and receive insights instantly. Qualetics gives you the tools to make informed decisions on the fly. Why Choose Qualetics: Simplicity Meets Innovation: Qualetics brings the power of AI to your fingertips without the complexity of coding. Diverse Model Library: Access a rich library of pre-trained models for a wide array of applications. Adaptive Learning: Benefit from self-learning AI models that adapt to evolving data patterns. Versatile Data Processing: Process text, image, document, audio, and video data seamlessly in one platform. Collaborative Environment: Foster collaboration with multi-tenancy support for teams of any size. Security-First Approach: Ensure the security and governance of your AI initiatives with our robust measures. Real-Time Insights: Make informed decisions with real-time observability features, putting you in control. Embark on your AI journey with Qualetics and experience the future of AI, simplified. Explore possibilities, innovate effortlessly! **Average Rating:** 4.5/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Qualetics?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Qualetics?** - **Seller:** [Qualetics Data Machines](https://www.g2.com/sellers/qualetics-data-machines) - **Year Founded:** 2018 - **HQ Location:** Princeton, US - **LinkedIn® Page:** https://www.linkedin.com/company/qualetics (4 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 25% Enterprise, 25% Mid-Market #### What Are Qualetics's Pros and Cons? **Pros:** - Customer Insights (1 reviews) - Data Analytics (1 reviews) - Features (1 reviews) - Insights (1 reviews) - Personalization (1 reviews) **Cons:** - Dashboard Issues (1 reviews) - Limited Customization (1 reviews) - Limited Features (1 reviews) - Limited Flexibility (1 reviews) ### 13. [Tazi](https://www.g2.com/products/tazi/reviews) TAZI is an Adaptive Machine Learning platform serving business users. TAZI has been chosen as a Cool Vendor in Core AI Technologies by Gartner and is cited as a Responsible, Explainable AI vendor in various analyst reports. Also, Data Science Central considered TAZI as "The Next Generation of Auto ML”. TAZI platform is based on an architecture combining 30+ years of experience and 23 patents in AI. TAZI enables business experts (and data scientists) to easily create, update, deploy and take actions with ML. TAZI models are understandable and learn continuously from streaming data and humans. TAZI helps insurance, retail, pharma, health, finance, manufacturing, and telco industries in making smarter business decisions via democratizing AI. TAZI provides various solutions such as customer retention, churn prediction, sales forecasting, and demand prediction to many companies including Fortune 500 from its offices in San Francisco and Istanbul. We enable human and machine intelligence to work together! **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Tazi?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Tazi?** - **Seller:** [Tazi AI Systems](https://www.g2.com/sellers/tazi-ai-systems) - **Year Founded:** 2017 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/taziai/ (31 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Small-Business ### 14. [Trendskout](https://www.g2.com/products/trendskout/reviews) Automate your Business with ready to use Machine Learning **Average Rating:** 3.0/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Trendskout?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Trendskout?** - **Seller:** [Trendskout](https://www.g2.com/sellers/trendskout) - **Year Founded:** 2019 - **HQ Location:** Ghent, BE - **LinkedIn® Page:** https://www.linkedin.com/company/trendskout (17 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 15. [Ultralytics](https://www.g2.com/products/ultralytics/reviews) Ultralytics is a prominent player in the field of vision AI, specializing in advanced computer vision solutions through its innovative YOLO (You Only Look Once) models. Designed to assist users in various industries, Ultralytics' technology enables real-time object detection and image analysis, making it an essential tool for businesses looking to leverage artificial intelligence for enhanced operational efficiency and decision-making. Targeted at a diverse audience that includes professionals in manufacturing, healthcare, transportation, agriculture, and retail, Ultralytics' offerings cater to organizations seeking to implement AI-driven solutions. The versatility of the YOLO models allows users to address a wide range of use cases, from automating quality control in manufacturing to improving patient outcomes in healthcare settings. By providing accessible and efficient AI tools, Ultralytics empowers businesses to harness the power of computer vision, ultimately driving innovation and growth. Key features of Ultralytics' technology include its remarkable speed and accuracy in image processing, which allows for the analysis of 1.6 billion images daily. This capability is complemented by the ability to train 5 million models per day, ensuring that users have access to the most up-to-date and effective AI tools. The YOLO models are designed to be user-friendly, enabling users with varying levels of technical expertise to implement and benefit from the technology without extensive training or resources. The unique selling points of Ultralytics lie in its commitment to AI accessibility and efficiency. By providing open-source solutions with extensive community support, the company fosters collaboration and innovation within the AI space. The impressive track record of over 110,000 GitHub stars and more than 100 million downloads highlights the widespread adoption and trust in Ultralytics' models. As industries continue to evolve and embrace digital transformation, Ultralytics remains at the forefront, offering cutting-edge solutions that meet the demands of a rapidly changing technological landscape. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Ultralytics?** - **Natural Language Understanding:** 3.3/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Ultralytics?** - **Seller:** [Ultralytics](https://www.g2.com/sellers/ultralytics) - **Company Website:** https://ultralytics.com - **Year Founded:** 2022 - **HQ Location:** 5001 Judicial Way Frederick, MD 21703, USA - **Twitter:** @ultralytics (8,541 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/ultralytics (37 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Ultralytics's Pros and Cons? **Pros:** - Deployment Ease (2 reviews) - Ease of Use (2 reviews) - Efficiency (2 reviews) - AI Technology (1 reviews) - Automation (1 reviews) **Cons:** - Poor Documentation (2 reviews) - AI Limitations (1 reviews) - Confusing Documentation (1 reviews) - Deployment Issues (1 reviews) - Insufficient Learning Resources (1 reviews) ### 16. [Wallaroo.ai](https://www.g2.com/products/wallaroo-ai/reviews) Easy Production AI at Scale: Any Model, Any Hardware, Anywhere. Purpose built for production AI, so AI teams stay lean and nimble. Enabling you to get to value fast for your cloud analytics, edge AI and gen AI initiatives. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Wallaroo.ai?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Wallaroo.ai?** - **Seller:** [Wallaroo](https://www.g2.com/sellers/wallaroo) - **Year Founded:** 2017 - **HQ Location:** New York, US - **Twitter:** @Wallarooai (737 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/wallarooai (44 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 17. [Zepl](https://www.g2.com/products/zepl/reviews) Zepl let you use data science to analyze your cloud data warehouse in minutes. Customers use Zepl for all kinds of use cases, including predictive analytics, marketing analytics, preventive maintenance, security, anomaly detection, sales forecasting, product recommendations and more. Zepl is an extensible, cloud-based data science and analytics platform for enterprise teams. With Zepl, teams of data analysts and data scientists can use Python, R, Spark, Scala, and SQL to find insights and make predictions about their most important business challenges, as well as package and present their findings using built-in advanced visualizations. **Average Rating:** 4.4/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Zepl?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Zepl?** - **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot) - **Year Founded:** 2012 - **HQ Location:** Boston, Massachusetts - **Twitter:** @DataRobot (19,254 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2672915/ (870 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Small-Business ### 18. [Afinsys](https://www.g2.com/products/afinsys/reviews) Our platform generates and repairs scraping code, adapting to website changes on the fly. With our no-code, easy-to-use interface, companies can scale their web data extraction efforts without the tedious task of building scraping bots for each individual website. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Afinsys?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 6.7/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Afinsys?** - **Seller:** [Affinsys](https://www.g2.com/sellers/affinsys) - **Year Founded:** 2017 - **HQ Location:** Dubai, AE - **LinkedIn® Page:** https://www.linkedin.com/company/affinsys-ai/ (73 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 19. [Altair Knowledge Studio](https://www.g2.com/products/altair-knowledge-studio/reviews) Advanced Machine Learning and Artificial Intelligence Data scientists and business analysts use Altair to generate actionable insight from their data. Knowledge Studio is a market-leading easy to use machine learning and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code. A recognized analytics leader, Knowledge Studio brings transparency and automation to machine learning with features such as AutoML and Explainable AI without restricting how models are configured and tuned, giving you control over model building. Key Features: - No Code Machine Learning Modeling - Transparent, Explainable AI - Predictive to Prescriptive Analytics **Average Rating:** 4.0/5.0 **Total Reviews:** 5 **How Do G2 Users Rate Altair Knowledge Studio?** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) **Who Is the Company Behind Altair Knowledge Studio?** - **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7) - **Year Founded:** 1985 - **HQ Location:** Troy, MI - **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (3,169 employees on LinkedIn®) - **Ownership:** NASDAQ:ALTR - **Total Revenue (USD mm):** $458 **Who Uses This Product?** - **Company Size:** 80% Enterprise, 20% Small-Business ### 20. [Apache Zeppelin packaged by Data Science Dojo](https://www.g2.com/products/apache-zeppelin-packaged-by-data-science-dojo/reviews) Apache Zeppelin is an open-source tool that equips you with a web-based notebook that can be used for machine learning operations, interactive data analytics, visualization, and exploration. Vibrant designs and pictures generated can save time for users in the identification of key trends in data and ultimately accelerates the decision-making processes. This offer not only contains different pre-installed interpreters but also allows you to plug in your own various language backends for desirability. Apache Zeppelin supports many data sources which allow you to synthesize your data to visualize into interactive plots and charts. You can also create dynamic forms in your notebook and can share your notebook with collaborators. Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Apache Zeppelin packaged by Data Science Dojo?** - **Application:** 6.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 8.3/10 (Category avg: 8.2/10) **Who Is the Company Behind Apache Zeppelin packaged by Data Science Dojo?** - **Seller:** [Data Science Dojo](https://www.g2.com/sellers/data-science-dojo) - **HQ Location:** Redmond, US - **Twitter:** @DataScienceDojo (226,921 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/data-science-dojo (185 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 21. [Artivatic](https://www.g2.com/products/artivatic/reviews) Artivatic empowers insurance, broker & re-insurance businesses and developers to re-imagine insurance products for the next billion users. Artivatic builds low-cost, modular API infrastructure so that you can go live in a matter of days, not months. The Artivatic is developing its propertioery cutting edge solutions to enable enterprises for 1 Billion people to get access to insurance, financial and health benefits with alternative data sources to increase their productivity, efficiency, automation power, and profitability, hence improving their way of doing business more intelligently seamlessly. Artivatic offers insurance underwriting, distribution, sales, agent efficiency, enabling branches /offices to go digital, fraud, prediction, personalization, recommendation, risk profiling, consumer profiling intelligence, KYC Automation & Compliance, automated decisions, monitoring, claims processing, sentiment/psychology behaviour, auto insurance claims, travel insurance, disease prediction, device based health profiling, wellness, APIs, and more. It enables businesses to have in-depth multi source data focused intelligence & decisions. Our Pilots have demonstrated more than 50% of efficiency, 90% reduction in turnaround time, 80% better fraud identification, 70% less cost reduction, more than 60% consumer engagement, more than 40% better risk delinquencies identification and intelligence in near real time. Call us at 08041502526 for more info or write to layak@artivatic.ai **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Artivatic?** - **Seller:** [Artivatic.ai](https://www.g2.com/sellers/artivatic-ai) - **Year Founded:** 2018 - **HQ Location:** Gurugram, IN - **LinkedIn® Page:** https://www.linkedin.com/company/artivatic (107 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 22. [C3 AI Suite](https://www.g2.com/products/c3-ai-suite/reviews) C3 Ex Machina allows you to fuse together and visually explore data across multiple datasets to build smart customer segments, predict asset failure, and understand your future business needs. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate C3 AI Suite?** - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind C3 AI Suite?** - **Seller:** [C3.ai](https://www.g2.com/sellers/c3-ai) - **Year Founded:** 2009 - **HQ Location:** Redwood City, CA - **Twitter:** @C3IoT (76 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/c3-ai/ (1,346 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 23. [Chooch](https://www.g2.com/products/chooch/reviews) Chooch makes cameras intelligent. The Chooch AI Vision platform combines the power of Generative AI and Computer Vision technology to help businesses gain real-time actionable insights from their video and visual data. Chooch's AI technology instantly detects specific visuals, objects, and actions in videos and images, including critical anomalies and instantly comprehends their significance, sending real-time alerts to business systems to initiate further action. It does this in a fraction of the time a human being could. Businesses use Chooch to automate repetitive, manual video review tasks, making searching video data more efficient and allowing businesses to reallocate human resources to higher value activities. Chooch is being used across many different applications including detecting retail theft, monitoring workplace safety, detecting weapons, monitoring self-check out, digital asset management, and more. **Average Rating:** 4.0/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Chooch?** - **Application:** 10.0/10 (Category avg: 8.5/10) - **Managed Service:** 10.0/10 (Category avg: 8.3/10) - **Natural Language Understanding:** 10.0/10 (Category avg: 8.2/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Chooch?** - **Seller:** [Chooch](https://www.g2.com/sellers/chooch) - **Year Founded:** 2015 - **HQ Location:** San Mateo, California - **Twitter:** @Chooch_AI (1,720 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/chooch/ (50 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Small-Business, 33% Mid-Market ### 24. [CoPilot-AI](https://www.g2.com/products/copilot-ai-copilot-ai/reviews) CoPilot-AI is an advanced tactical decision-support system designed to enhance wildfire management through the integration of cutting-edge Artificial Intelligence technologies. Developed by an interdisciplinary team at Maynooth University, CoPilot-AI leverages drones, robotics, sensors, and machine learning to construct a Common Operational Picture (COP) digital platform. This platform facilitates smart multi-thematic sensor data capture, automated data fusion, deep learning analysis, and effective information sharing, thereby enabling more informed and timely decision-making during wildfire events. Key Features and Functionality: - AI-Driven Data Analysis: Utilizes machine learning algorithms to process and interpret complex data sets, providing actionable insights for wildfire management. - Drone and Robotic Sensing Platforms: Employs advanced drones and robotic systems for real-time monitoring and data collection in wildfire-affected areas. - Automated Data Fusion: Integrates data from various sensors and sources to create a comprehensive and cohesive operational picture. - Dynamic Terrain and Scene Analysis: Analyzes changing environmental conditions to assess risk and predict wildfire behavior. - Multi-Criteria Risk Modeling: Evaluates multiple factors to model and predict potential wildfire risks, aiding in proactive management strategies. Primary Value and Problem Solved: CoPilot-AI addresses the critical need for improved wildfire response and management by providing a unified platform that enhances situational awareness and decision-making capabilities. By integrating AI technologies with real-time data collection and analysis, it enables emergency response teams to make informed decisions swiftly, reducing the threat to human life and minimizing economic and environmental damage caused by wildfires. This system streamlines coordination among various agencies and organizations involved in wildfire response, ensuring a more efficient and effective approach to managing these natural disasters. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind CoPilot-AI?** - **Seller:** [CoPilot-AI](https://www.g2.com/sellers/copilot-ai-bb04b437-7b24-47ca-b423-70df19003b15) - **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 ### 25. [Data Science Wizards](https://www.g2.com/products/data-science-wizards/reviews) Data Science Wizards (DSW) is building UnifyAI OS - the Enterprise AI Operating System (AI OS) for regulated and hybrid enterprises. As AI moves from experimentation into execution-especially with the rise of agentic systems- enterprises face a fundamental question: How do we build, deploy, operate, and govern AI continuously, safely, and at scale as part of the enterprise itself? DSW Enterprise AI OS enables enterprises to: • Build, integrate, deploy, orchestrate, govern, monitor, and continuously operate AI/ML and agentic workloads • Treat AI use cases, AI workloads, and AI applications as long-running production systems • Enforce governance-as-code at runtime, not through static policy documents • Retain full ownership of all AI artifacts and source code built by the enterprise • Run AI entirely inside customer-controlled environments (on-premises, local, private cloud, hybrid) • Integrate a broad AI ecosystem without vendor lock-in DSW is defining a new infrastructure category: Enterprise AI Operating Systems. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Data Science Wizards?** - **Seller:** [Data Science Wizards](https://www.g2.com/sellers/data-science-wizards) - **Year Founded:** 2019 - **HQ Location:** Thane, IN - **LinkedIn® Page:** https://www.linkedin.com/company/data-science-wizards (65 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ## 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.