Solutions Review’s listing of the best data science and machine learning platforms is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best data science platforms via a meta-analysis of real user sentiment through the web’s most trusted business software review sites.
The editors at Solutions Review have developed this resource to assist buyers in search of the best data science platforms to fit then needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best data science platforms providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.
Note: Companies are listed in alphabetical order.
Platform: Altair Knowledge Studio
Related products: Altair Knowledge Studio for Apache Spark, Altair Knowledge Seeker
Description: Altair Knowledge Works (formerly Datawatch) offers an advanced data mining and predictive analytics workbench called Knowledge Studio. The product features patented Decision Trees, Strategy Trees, and a workflow and wizard-driven graphical user interface. It also includes capabilities for data preparation tasks, visual data profiling, advanced predictive modeling, and in-database analytics. Users can import and export using common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS.
Platform: Anaconda Enterprise
Related products: Anaconda Distribution, Anaconda Team Edition
Description: Anaconda is an open source Python and R data science platform. The tool enables you to perform data science and machine learning on Linux, Windows, and Mac OS. The product allows users to download more than 1,500 Python and R data science packages, manage libraries, dependencies, and environments, and analyze data with Dask, NumPy, pandas, and Numba. You can then visualize results generated in Anaconda with Matplotlib, Bokeh, Datashader, and Holoviews.
Platform: Databricks Unified Analytics Platform
Description: Databricks offers a cloud and Apache Spark-based unified analytics platform that combines data engineering and data science functionality. The product leverages an array of open source languages, and includes proprietary features for operationalization, performance and real-time enablement on Amazon Web Services. A Data Science Workspace enables users to explore data and build models collaboratively. It also provides one-click access to preconfigured ML environments for augmented machine learning with popular frameworks.
Platform: Dataiku DSS
Description: Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch. Users can then apply machine learning and data science techniques to build and deploy predictive data flows.
Related products: Automated Machine Learning, Automated Time Series, MLOps
Description: DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is powered by open source algorithms and can be leveraged on-prem, in the cloud or as a fully-managed AI services. DataRobot includes three independent but fully integrated tools (Automated Machine Learning, Automated Time Series, MLOps), and each can be deployed in multiple ways to match business needs and IT requirements.
Platform: Domino Data Science Platform
Description: Domino Data Lab offers an enterprise data science platform that allows data scientists to build and run predictive models. The product helps organizations with the development and delivery of these models via infrastructure automation and collaboration. Domino provides users access to a Data Science Workbench that provides open source and commercial tools for batch experiments, as well as Model Delivery so they can publish APIs and web apps or schedule reports.
Related products: Sparkling Water, H2O Driverless AI, H2O Q
Description: H2O.ai offers a range of AI and data science platforms. Its H2O platform is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O supports widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O has also developed AutoML functionality that automatically runs through all the algorithms to produce a leaderboard of the best models.
Platform: KNIME Analytics Platform
Related products: KNIME Server
Description: KNIME Analytics is an open source platform for creating data science. It enables the creation of visual workflows via a drag-and-drop-style graphical interface that requires no coding. Users can choose from more than 2000 nodes to build workflows, model each step of analysis, control the flow of data, and ensure work is current. KNIME can blend data from any source and shape data to derive statistics, clean data, and extract and select features. The product leverages AI and machine learning, and can visualize data with classic and advanced charts.
Related products: Simulink
Description: MathWorks MATLAB combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, tested, and fully documented. MATLAB apps let you see how different algorithms work with your data as well.
Platform: RapidMiner Studio
Related products: RapidMiner Turbo Prep, RapidMiner Auto Model, RapidMiner Model Ops
Description: RapidMiner offers a data science platform that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists need, but simplifies AI for everyone else via a visual user interface that streamlines the process of building and understanding complex models.
Platform: SAS Platform
Related products: SAS Model Manager, SAS Visual Analytics, SAS Visual Data Mining & Machine Learning, SAS Viya
Description: SAS offers a strong suite of advanced analytics and data science products. Its SAS Platform provides access to data in any format and from any source, automated data preparation, and data lineage and model management. SAS Visual Data Mining and Machine Learning automatically generates insights for common variables across models. It also features natural language generation for creating project summaries. SAS Model Manager enables users to register SAS and open source models within projects or as standalone models.
Platform: TIBCO Data Science
Related products: TIBCO Spotfire, TIBCO Jaspersoft
Description: TIBCO offers an expansive product portfolio for modern BI, descriptive and predictive analytics, and streaming analytics and data science. TIBCO Data Science lets users do data preparation, model building, deployment and monitoring. It also features AutoML, drag-and-drop workflows, and embedded Jupyter Notebooks for sharing reusable modules. Users can run workflows on TIBCO’s Spotfire Analytics and leverage TensorFlow, SageMaker, Rekognition and Cognitive Services to orchestrate open source.
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