The 5 Major Players in Data Science and Machine Learning Platforms

The 5 Major Players in Data Science and Machine Learning Platforms

Part of our ongoing coverage of the data and analytics marketplace involves the various solution providers that make up the space. Vendors that offer data science products come in many different shapes and sizes, and it’s common for the eye to focus on the shiny new toy; the providers offering something we’ve never seen before. While those trends are certainly worth keeping tabs on, we recognize that reporting on the pillars of the market may be even more beneficial given the ease with which they are recognized.

The following providers have recently been named leaders in Gartner’s Magic Quadrant for Data Science and Machine-Learning Platforms, and while each company’s market share differs, these tools shape the foundation of this software market. Emerging providers can only hope to replicate the kind of success that these cornerstones have earned over a period of time. These providers stand out as rock-solid cornerstones that offer tools for a wide variety of use cases, making them the most trustworthy of the bunch.

KNIMEKNIME is the top dog in this marketplace, according to Gartner. The vendor touts a commitment to open source, low total cost of ownership. and a cohesive platform for many data science skill levels. KNIME features more than 1500 modules, hundreds of ready-to-run examples, and a wide set of advanced algorithms. Founded in 2008, the company is based in Switzerland.

AlteryxAlteryx acquired data science tools provider Yhat shortly after going public in the first quarter of 2017. The company followed up on that acquisition by launching Alteryx Promote, a component that enables data scientists to deploy predictive models directly into business systems. Alteryx offers an expansive platform with many noteworthy analytic features, and their foray into data science tools figures to be a boon to citizen data scientists.

SASSAS offers advanced and predictive analytic technologies, which include forecasting, text analytics, and decision trees. Data scientists can extend these capabilities using the integrated Visual Statistics tool. SAS caters to a wide range of verticals, and users particularly enjoy ease of use in accessing Hadoop and NoSQL data. However, the platform’s UI is lacking, and interoperability between separate SAS products could be better. The hope is that its expanded Viya rollout will help to quell some of these issues.

RapidMinerRapidMiner offers a unified platform for data science teams that includes data preparation, machine learning and predictive model deployment. The product touts a community of more than a quarter-million data science experts, as well as a marketplace that keeps pace with evolving trends. RapidMiner’s 60+ connectors provide access to any type of data, and users can run workflows in-memory or in-Hadoop. The company has raised $36 million in venture capital since its founding in 2007. offers an open source machine learning product that allows users to build ‘smart’ applications. The solution enables data scientists and developers to import algorithms into existing applications as well. H2O features a global community of nearly 130,000 data scientists and more than 12,000 organizations. The vendor has raised more than $73 million in venture capital, with its most recent round coming in November 2017.

Tim King
Follow Tim