The 16 Best Data Science and Machine Learning Platforms

Users can utilize Data Science and Machine Learning Platforms to create, deploy, and monitor machine learning algorithms. These platforms combine data with intelligent decision-making algorithms, allowing developers to build a commercial solution. Some systems include prebuilt algorithms and simple workflows with features like drag-and-drop modeling and visual interfaces that make it simple to connect relevant data to the end solution, while others demand more development and coding experience. In addition to other machine learning capabilities, these algorithms may incorporate image identification, natural language processing, voice recognition, and recommendation systems.

The Best Data Science and Machine Learning Platforms

• Altair

Platform: Altair Knowledge Works

Related products: Altair Knowledge Studio for Apache Spark, Altair Panopticon, Altair Knowledge Studio, Altair Knowledge Hub, Altair Monarch

Description: Through its Knowledge Works portfolio, Altair (previously Datawatch) offers a number of products, the most notable of which being Knowledge Studio, an advanced data mining, and predictive analytics workbench. The software includes patented Decision Trees and Strategy Trees, as well as a workflow and wizard-driven user interface. It also has data preparation, visual data profiling, powerful predictive modeling, and in-database analytics capabilities. Common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS, can be used to import and export data.

• Alteryx

Platform: Alteryx Analytics Process Automation

Related products: Alteryx Intelligence Suite, Alteryx Designer, Alteryx Connect, Alteryx Server, Alteryx Promote

Description: Alteryx has a software suite that includes data science and machine learning capabilities. The self-service platform has more than 260 drag-and-drop building components, including Alteryx Designer, which automates data preparation, data blending, reporting, predictive analytics, and data science. Alteryx makes it simple to see variable correlations and distributions, as well as to choose and compare algorithm performance. The software can be deployed in the cloud, behind your own firewall, or in a hosted environment with no coding necessary.

• Anaconda

Platform: Anaconda Enterprise

Related Products: Anaconda Distribution, Anaconda Team Edition

Description: Anaconda offers a variety of product editions with data science and machine learning capabilities. Anaconda Enterprise, an open-source Python and R platform, is the company’s main offering. On Linux, Windows, and Mac OS, the tool allows you to undertake data science and machine learning. Users may use Anaconda to download over 1,500 Python and R data science packages, manage libraries, dependencies, and environments, and analyse data using Dask, NumPy, pandas, and Numba. Bokeh, Matplotlib, Datashader, and Holoviews can then be used to visualise the Anaconda results.

• Databricks

Platform: Databricks Unified Analytics Platform

Description: Databricks is a cloud-based unified analytics platform based on Apache Spark that integrates data engineering and data science features. On Amazon Web Services, the solution uses a variety of open-source languages and contains proprietary capabilities for operationalization, performance, and real-time enablement. Users can collaborate to analyse data and construct models in a Data Science Workspace. It also offers one-click access to preset machine learning environments for augmented machine learning using common frameworks.

• Dataiku

Platform: Dataiku Data Science Studio (DSS)

Description: Dataiku is a sophisticated analytics platform that allows businesses to build their own data tools. For both data analysts and data scientists, the company’s main solution has a team-based user interface. Dataiku’s single development and deployment platform gives you instant access to all the functionality you need to build data tools from the ground up. Users can then create and implement predicted data flows using machine learning and data science methodologies.

• DataRobot

Platform: DataRobot Enterprise AI Platform

Related products: Automated Machine Learning, Paxata Data Preparation, MLOps, Automated Time Series.

Description: DataRobot provides an enterprise AI platform that automates the whole AI development, deployment, and maintenance process. The software is based on open-source methods and can be used on-premises, in the cloud, or as a fully managed AI service. DataRobot consists of several separate but completely integrated products (Paxata Data Preparation, Automated Machine Learning, Automated Time Series, MLOps, and AI apps), each of which may be used in a variety of ways to meet business and IT needs.

• Domino Data Lab

Platform: Domino Data Science Platform

Related products: Domino Model Monitor

Description: Domino Data Lab provides an enterprise data science platform for data scientists to create and test prediction models. Through infrastructure automation and collaboration, the solution assists organisations in the creation and delivery of these models. Domino gives users access to a data science Workbench with free source and commercial batch experiment tools, as well as Model Delivery, which allows them to deploy APIs and web apps and schedule reports.

• Google

Platform: Google Cloud AI Platform

Related products: Google Cloud AutoML, Google Cloud Data Fusion, Google AI Platform Notebooks, Google TensorFlow, Google BigQuery ML

Description: Google Cloud AI is one of the most comprehensive machine learning stacks in the industry, with a growing number of solutions for a variety of applications. The product is fully controlled and comes with strong governance and models that are easy to understand. A built-in Data Labeling Service, AutoML, model validation via AI Explanations, a What-If Tool to help comprehend model outputs, cloud model deployment with Prediction, and MLOps via the Pipeline tool are some of the key features.

• H2O.ai

Platform: H2O Driverless AI

Related products: H2O 3, H2O AutoML for ML, H2O Sparkling Water for Spark Integration, H2O Wave

Description: H2O.ai offers a variety of AI and data science technologies, with its commercial platform H2O Driverless AI being the most notable. Driverless AI is a distributed in-memory machine learning platform with linear scalability that is completely open-source. H2O supports a variety of commonly used statistics and machine learning algorithms, such as gradient boosted machines, generalized linear models, deep learning, and more. H2O has also created the AutoML feature, which runs all of the algorithms automatically to produce a scoreboard of the best models.

• IBM

Platform: IBM Watson Studio

Related products: IBM SPSS Modeler, IBM Cloud Pak for Data, IBM Watson Machine Learning, IBM Decision Optimization.

Description: Users can utilise IBM Watson Studio to create, run, and manage AI models at scale on any cloud. IBM Cloud Pak for Data, the company’s core data and AI platform, includes the offering. With explainable AI, you can automate AI lifecycle management, regulate and secure open-source notebooks, visually prepare and create models, deploy and operate models through one-click integration, and manage and monitor models. Users may employ open-source frameworks like PyTorch, TensorFlow, and scikit-learn with IBM Watson Studio’s flexible architecture.

• KNIME

Platform: KNIME Analytics Platform

Related products: KNIME Server

Description: KNIME Analytics is a free and open-source data science tool. It allows users to create visual processes using a drag-and-drop graphical interface that does not require coding. Users can construct workflows, model each phase of analysis, regulate data flow, and guarantee work is current by selecting from over 2000 nodes. KNIME can combine and shape data from any source to create statistics, clean data, and extract and select characteristics. The software uses AI and machine learning to show data using both traditional and advanced charts.

• MathWorks

Platform: MATLAB

Related products: Simulink

Description: MathWorks MATLAB combines an iterative analysis and design environment with a programming language that represents matrix and array mathematics natively.The Live Editor is included, allowing you to create scripts that combine code, output, and formatted text into an executable notebook. MATLAB toolboxes that have been professionally designed, tested, and documented. You can also explore how different algorithms function with your data using MATLAB programmes.

• Microsoft

Platform: Azure Machine Learning

Related products: Azure Data Catalog, Azure Data Factory, Azure HDInsight, Azure DevOps, Azure Databricks, Power BI

Description: Developers and data scientists can use the Azure Machine Learning service to create, train, and deploy machine learning models. A code-first and drag-and-drop designer, as well as automated machine learning, make the product productive for users of all skill levels. It also has extensive MLops capabilities that work with conventional DevOps workflows. Users can comprehend models with interpretability and fairness, as well as protect data with differential privacy and confidential computing, according to the service’s emphasis on responsible machine learning. Open-source frameworks and languages such as MLflow, ONNX, Kubeflow, PyTorch, Python, TensorFlow, and R are supported by Azure Machine Learning.

• RapidMiner

Platform: RapidMiner Studio

Related products: RapidMiner AI Hub, RapidMiner Go, RapidMiner Notebooks, RapidMiner AI Cloud

Description: RapidMiner is a data science platform that allows employees of all skill levels to design and operate AI solutions throughout the company. From data discovery and data preparation to model creation, model deployment, and model operations, the package covers the entire AI production process. RapidMiner gives data scientists the depth they need, but it also makes AI easier for everyone else with a visual user interface that makes developing and comprehending complex models a breeze.

• SAS

Platform: SAS Visual Data Mining and Machine Learning

Related products: SAS Visual Machine Learning, SAS Viya, SAS Visual Data Science, SAS Visual Data Decisioning, SAS Data Science Programming.

Description: SASVisual Data Mining and Machine Learning are among the company’s sophisticated analytics and data science technologies. Access to data in any format and from any source is available, as well as automated data preparation, data lineage, and model management. SAS Visual Data Mining and Machine Learning produces shared variable insights across models effortlessly. For developing project summaries, it also has natural language generation. Users can register SAS and open-source models within projects or as standalone models using the SAS Model Manager that comes with the software.

• TIBCO

Platform: TIBCO Data Science

Related products: TIBCO Spotfire, TIBCO Streaming

Description: TIBCO has a wide range of products for modern business intelligence, descriptive and predictive analytics, streaming analytics, and data science. Data preparation, model construction, deployment, and monitoring are all possible with TIBCO Data Science. AutoML, drag-and-drop workflows, and embedded Jupyter Notebooks for sharing reusable modules are also included. Users can use TIBCO’s Spotfire Analytics to perform workflows and orchestrate open-source using TensorFlow, SageMaker, Rekognition, and Cognitive Services.

Final Thoughts

Because data scientists are responsible for developing effective solutions to data science problems, it is up to them to select the finest tools to aid them in this endeavor. Expert data scientists must exercise caution when selecting a data science and machine-learning platform. They must also select a platform provided by a company that shares their mission.