BOSTON, Mass., February 22, 2018—RapidMiner™, the company that delivers real data science, fast and simple, announced another quarter of record revenue for its Q4 2017. Q4 builds on a strong year that saw RapidMiner exceed its goals for new customer acquisition, user community growth, and deployments by the largest global enterprises.
RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment. Organizations can build machine learning models and put them into production faster than ever, using RapidMiner’s lightning fast visual workflow designer and automated modeling capabilities. RapidMiner eliminates the complexities of cutting edge data science by making it easy to use the latest machine learning algorithms and technologies like Tensorflow, Hadoop, and Spark.
“RapidMiner finished the year with a great Q4 2017, leading to our best year ever,” said Peter Lee, chief executive officer at RapidMiner. “We consistently delivered innovative new products for data scientists, while helping customers in nearly every industry drive revenue, reduce costs, and avoid risks. The investments we’ve made across the company, especially in our engineering team, will accelerate our market leadership position in 2018. It’s a great time to join the more than 330,000 RapidMiner users around the world.”
Global enterprises use RapidMiner for a wide variety of data science projects across customer analytics, operational analytics, and risk analytics. In Q4 2017, RapidMiner saw increased demand from financial services organizations, including the following new customers:
- One of the world’s largest investment companies, who selected RapidMiner to improve the productivity of their data science team in order to innovate new products with lower delivery costs and higher returns.
- A multinational insurance group who selected RapidMiner for fraud analytics and customer segmentation applications, integrating with a variety of technologies including Microsoft Azure HDInsight and Apache Spark.
- A global banking and financial services organization who selected RapidMiner for a range of risk analytics projects, including fraud detection and compliance.
RapidMiner 8.0 was introduced in Q4 2017, providing a significant architectural upgrade to support enterprise-scale data science deployments. Select new capabilities included:
- Horizontal scalability. A new distributed architecture in RapidMiner Server allows enterprises to fluidly scale as their data science teams grow and more models are deployed into production.
- Improved stability. RapidMiner Server now supports containerized job executions to create a highly stable environment.
- New user interface. RapidMiner Server introduced a new user interface designed to improve productivity for common administration tasks.
- New and improved machine learning algorithms. RapidMiner Studio enhancements include regression trees, extremely randomized trees, a fuzzy operator search, and improved operator documentation.
Other company news from Q4 2017:
- RapidMiner added industry veteran Jeff Bashaw to its executive team as Vice President of Channels. Bashaw joins RapidMiner from Qlik, where he held the role of Vice President of Sales.
- RapidMiner launched season two of the popular 5 Minutes with Ingo video series on data science and machine learning.
- The RapidMiner community grew by 50% in 2017, with over 330,000 data scientists in 150 countries.
RapidMiner builds a software platform for data science teams that unites data prep, machine learning, and predictive model deployment. Organizations can build machine learning models and put them into production faster than ever, using RapidMiner’s lightning fast visual workflow designer and automated modeling capabilities. RapidMiner eliminates the complexities of cutting edge data science by making it easy to use the latest machine learning algorithms and technologies like Tensorflow, Hadoop, and Spark. More than 300,000 data scientists in virtually every industry use RapidMiner products on premise or in the cloud to drive revenue, reduce costs, and avoid risks.