Alpine’s Drag-and-Drop Approach to Big Data Machine Learning

Alpine Data’s latest version of their advanced analytics platform, Chorus 6, features several updates designed to bridge the gap between business decisions and machine learning.

Chorus 6 is designed to allow business users and data scientists to build and share drag-and-drop machine learning and ETL workflows. Business users can thus integrate machine learning without having to learn data science or retrain on new systems.

Updates to the platform, announced May 25, extend big data and real-time analytics capabilities.

Chorus 6 now integrates Jupyter Notebooks to allow fast data analysis in Python, and provides APIs to quickly pull data from Hadoop clusters and relational databases. Notebooks are executed and managed server-side to make team collaboration and visibility easy.

“Successful machine learning strategies start with a well-posed business problem, and more than ninety percent of the value in machine learning is created outside the algorithm,” Steven Hillion, chief product officer of Alpine Data, said in a press release. “The beauty of Chorus 6 is enabling enterprise teams to organize and apply rigor to the data science process all the way from defining the business problem to an operational solution.”

Chorus 6 also now integrates Trifacta’s data transformation and cleansing solutions. Users can clean, enhance, discover, structure, validate and publish data for analysis.

One customer that uses Chorus is Lockheed Martin. The firm’s Collaborative Advanced Analytics and Data Sharing platform helps agencies analyze disease outbreaks, with the goal of preventing epidemics. “Chorus enables our data scientists to collaboratively develop advanced analytics at scale,” said Ravi Hubbly, senior technology manager of Lockheed Martin Information Systems & Global Solutions.

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