Operationalizing Data Science Using Cloud Foundry

August 10, 2016
SpringOne Platform 2016 Speaker: Lawrence Spracklen; Vice President of Engineering, Alpine Data Labs. Data science is undoubtedly becoming a key component of every company’s core strategy for growth and increased revenue potential. To meet this market demand, the big data industry has exploded with a variety of tools to address various pieces of the data science value chain, from model scoring, to notebook interfaces, to niche algorithmic techniques. However, despite the increase in innovation in this area, many insights generated by data science teams end up “dying on the vine”. There has to be a better way of deploying operational models to end users through intuitive interfaces that they can use everyday. In this session, we will demo how the joint solution between Alpine’s Chorus Platform and Cloud Foundry addresses this problem and closes the gap between data science insights and business value. We will demo an example of creating a machine learning model leveraging data within MPP databases such as Apache HAWQ or Greenplum Database integrated with the Chorus Platform and then deploying this as a micro service within Cloud Foundry as a scoring engine. This turn-key solution will show attendees how easy it is to plug in analytic insights into end user applications that scale, without going through lengthy development cycles.
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