The Coolest Data Science And Machine Learning Tool Companies Of The 2019 Big Data 100

Part 4 of CRN's 2019 Big Data 100 looks at the companies you need to know that provide data science and machine learning software technologies.

The Scientific Method

Data science, machine learning and artificial intelligence are big in big data right now, with data scientists and the tools they use playing an increasingly important role in analyzing and deriving value from big data.

The global market for data science platforms is expected to grow at a CAGR of nearly 39 percent between now and 2021 when it will reach $101.4 billion, according to a MarketsandMarkets forecast.

As part of the 2019 Big Data 100, we've put together a list of companies that provide data science and machine learning product.

This week CRN is posting the Big Data 100 list in a series of slide shows for vendors of business analytics software, data management and integration software, data science and machine learning tools, and big data systems and platforms.

Big Squid

Top Executive: CEO Chris Knoch

Big Squid develops the Kraken automated machine learning platform that is used by data scientists to assist with model development and by analysts and even business users to develop analytical insights from huge volumes of data. The company emphasizes Kraken's ease of use and ability to improve data accessibility and prescriptive actions.


Top Executive: CEO Florian Douetteau

Dataiku offers the Dataiku DSS collaborative data science platform that allows teams of data scientists, data analysts and engineers to explore, prototype, build and deploy AI- and machine learning-based systems for such tasks as data management, demand forecasting, spatial analytics, churn analytics, fraud detection, lifetime value optimization and analytical CRM.

In March the company launched Dataiku 5.1 with user experience upgrades, more customized coding features and improved regulatory compliance capabilities.


Top Executive: CEO Jeremy Achin

DataRobot develops an automated machine learning platform that streamlines the building and deployment of accurate predictive models. The system incorporates the knowledge, experience and best practices of some of the world's leading data scientists, according to the company.

In April the company said that customers had built 1 billion models on the DataRobot system running on AWS cloud.

Domino Data Lab

Top Executive: CEO Nick Elprin

Domino's data science platform provides a unified system where data scientists collaborate to build, validate, deliver and monitor business models, helping organizations institute data science as an enterprise-wide discipline. In March the company expanded the platform's functionality with new datasets, experiment manager and activity feed capabilities.

Top Executive: CEO Sri Satish Ambati

H2O has developed a number of open-source machine learning and artificial intelligence systems for building big data predictive analysis applications, including H2O, AutoML, Sparkling Water and H2O Driverless AI.


Top Executive: CEO Matthew Carroll

Immuta provides enterprise data management software that data scientists, data owners and data stewards use to locate, access, share, control and monitor data.

In April the company debuted the Immuta Automated Data Governance Platform with automated governance capabilities that data scientists and business analysts use to securely share data and scripts without violating data policies and industry regulations.


Top Executive: CEO Michael Berthold

The Knime data mining and data integration platform uses a "guided analytics" system to automate data science processes. In December the company released Knime Analytics Platform and Knime Server 3.7 with new interactive views and statistical tests.


Top Executive: CEO Peter Lee

RapidMiner's data science platform is used by data science and data analysis teams that perform data preparation, machine learning and predictive model deployment tasks. In October the company launched RapidMiner AI Cloud, a unified SaaS platform used by data scientists and business analysts to build, train, deploy and manage predictive models in the cloud.