Top Machine Learning vendors recommended by Gartner

Gartner’s 2019 Magic Quadrant for Data Science and Machine Learning Platforms published a list of top vendors of data science and machine learning. The report, previously titled the Magic Quadrant for Data Science Platforms and the Magic Quadrant for Advanced Analytics Platforms, evaluates vendors and software products that enable expert data scientists, citizen data scientists and application developers to create, deploy and manage their own advanced analytical models.

In this article, we will look at Gartner’s recommended list of top Machine Learning vendors.

1. Anaconda

Anaconda is the most popular Python data science platform and the foundation of modern machine learning with over 13 million users and 150 + business customers. It offers Anaconda Enterprise 5.2, an interactive notebook concept-based data science development environment (this analysis excludes Conda Distribution Packages) that sees users exploiting open-source Python and R-based packages. Anaconda Enterprise delivers speed and scale data science and machine learning, unleashing the full potential of our customers ‘ data science and machine learning initiatives.

2. Dataiku

Dataiku offers Data Science Studio (DSS) that lets data scientists and analysts do machine learning on any (dirty) data, focusing on interdisciplinary collaboration and ease of use. It has a centralized data platform that moves businesses from analytics on a scale to enterprise AI. It also has a repository of best practices, machine learning and AI deployment / management shortcuts, and a centralized, controlled environment.

3. DataRobot

DataRobot offers a machine-learning platform for all-level data scientists to build and deploy accurate predictive models in a fraction of the time it took. In R, Python, Spark MLlib, H2O and other open source libraries, the DataRobot platform uses massively parallel processing to train and evaluate 1000 models. It searches through millions of possible algorithm combinations, pre-processing steps, features, transformations, and tuning parameters to deliver the best models for your target dataset and prediction.

4. H2O.ai

H2O.ai provides an open source machine learning platform to create smart applications. H2O’s core strength is its high-performance ML components, tightly integrated into several competing platforms. Data scientists and developers can import powerful algorithms into their applications. Over 5,000 organizations already use their technology to predict fraud, customer churn, etc. H20.ai provides insurance, healthcare, telecom, marketing, retail and manufacturing solutions.

5. Microsoft

Microsoft provides data science and ML software products. It offers Azure Machine Learning (including Azure Machine Learning Studio), Azure Data Factory, Azure HDInsight, Azure Databricks and Power BI in the cloud. Microsoft offers Machine Learning Server for on-site workloads.

6. RapidMiner

RapidMiner offers a software platform for data science teams that unites data prep, machine learning, and predictive model deployment. It includes RapidMiner Studio, RapidMiner Server, RapidMiner Cloud and RapidMiner Radoop. Organizations can build and manufacture machine-learning models faster than ever on a single platform. In over 150 countries, over 300,000 data scientists use RapidMiner products.

7. Google

Google’s core ML platform includes Cloud ML Engine, Cloud AutoML, TensorFlow, and recently announced BigQuery ML. Its ML components require other end – to – end Google components such as Google Cloud Dataprep, Google Datalab, Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, Google Data Studio, Kubeflow and Google Kubernetes Engine.

8. Alteryx

Alteryx is a leader in data science and analytics self-service that provides analysts with the unique ability to easily prepare, blend and analyze all their data using a repeatable workflow, then deploy and share analytics on a scale for deeper insights in hours. Analysts can connect and clean data from data warehouses, cloud applications, spreadsheets and other sources with their four software products, which comprise their data science platform, easily join these data together, then perform analytics — predictive, statistical, and spatial — using the same intuitive user interface without writing code. It includes Alteryx Connect, Alteryx Designer, Alteryx Server and Alteryx Promote. Alteryx customers range from the world’s largest and best-known brands.

9. Databricks

Founded by a team which created Apache Spark, Databricks provides a Unified Analytics Platform that combines data engineering and data science capabilities using a variety of open-source languages to collaborate and build data products. In addition to Spark, Amazon Web Services (AWS) offers proprietary features for security, reliability, operationalization, performance and real-time enabling. Users achieve faster time-to-value with Databricks by creating analytical workflows from ETL to interactive production exploration.

10. Datawatch (Angoss)

Datawatch acquired Angoss and its main data science product components. They include KnowledgeSEEKER, the most basic offering for citizen data scientists in a desktop context; KnowledgeSTUDIO, which includes many more models and capabilities than KnowledgeSEEKER; and KnowledgeENTERPRISE, a flagship product that includes the full range of capabilities.

11. Domino

Domino Data Lab is a platform that allows data scientists to build and run models. A comprehensive end-to-end solution designed for expert data scientists, the platform incorporates open-source and proprietary tool ecosystems while providing collaborative, reproductive and model development and deployment capabilities.

12. KNIME

KNIME contributes significantly to KNIME Analytics Platform, an open – source platform for intuitive, integrative data science. It has a commercial extension, KNIME Server, offering advanced features such as collaboration, automation, deployment, and management.

13. SAP

SAP offers SAP Predictive Analytics (PA), a platform with a number of components including Data Manager for Dataset Preparation and Feature Engineering, Automated Modeler for Citizen Data Scientists, Advanced ML Expert Analytics, and Predictive Factory for Operationalization. SAP PA is integrated with SAP HANA.

14. SAS

SAS provides numerous analytics and data science software products, including SAS Enterprise Miner (EM) and Visual Data Mining and Machine Learning (VDMML).

15. TIBCO Software

TIBCO Software has built a well-rounded and powerful analytics platform with the acquisition of enterprise reporting and modern BI platform vendors — Jaspersoft and Spotfire, descriptive and predictive analytics platform vendors — Statistica and Alpine Data, and a streaming analytics vendor — StreamBase Systems.

16. IBM

IBM is a visionary, highly visible data science and ML market vendor. But it lost ground in terms of both Vision completeness and executability, relative to other vendors. IBM has defined a clear product strategy and roadmap for both platforms: SPSS (including SPSS Modeler and SPSS Statistics) and Watson Studio, which incorporates the previous Data Science Experience (DSX) product of IBM but needs to demonstrate that its new approach can deliver consistent customer success over time.

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