New Datameer X Release Features Machine Learning Data Preparation

New Datameer X Release Features Machine Learning Data Preparation

Datameer has announced the release and general availability of Datameer X, a data preparation and exploration tool built for data science and machine learning professionals. The solution enables users to hasten machine learning and analytics cycles, as well as create data flows that feed data into machine learning models. Datameer X also includes automated functions that shape data for machine learning and large-scale data exploration that locates the best data for a project.

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Datameer offers a big data analytics platform that utilizes the native query engines for Hadoop and Spark. The company uses Hadoop for both storage and compute. Datameer is a fully extensible solution that can integrate with existing data warehouse or business intelligence tools. The San Francisco-based vendor specializes in assisting organizations that invest in data lakes and other big data environments that support analytics for four main use cases: customer analytics, operational analytics, enterprise data warehouse (EDW) optimization, and fraud and compliance.

New point-and-click machine learning encoding functions help to automate the process of shaping data to feed machine learning models. Excel-like pivot tables allow users to explore and organize data into tables with support for billions of records, hundreds of attributes, and thousands of categorical values. Datameer X also provides enhanced integrations with external data sources and destinations.

In a media statement, the company’s CEO Christian Rodatus said: “The new encoding and pivot table features in Datameer X will truly accelerate data preparation for machine learning initiatives and allow data scientists to focus on what they do best. The product’s point-and-click functionality lets data scientists find the best data and feed their models 10 to 20 times faster, eliminating errors along the way.”

Learn more about Datameer X.

Timothy King
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