LinkedIn open-sources internal tool to simplify machine learning projects

Only three days after previewing a slew of upcoming enhancements for Apache Kafka, LinkedIn Inc. is adding another item to its open-source analytics repertoire with the release of a likewise internally-developed tool that promises to simplify machine learning initiatives. And in particular, coordination across the different stages of the project lifecycle. Work on large-scale recommendation systems like the kind LinkedIn uses to refer members to groups and job postings that match their professional interests is typically divided between two teams. One is responsible for creating the mathematical models into which raw data is fed for analysis, while the other takes care of testing and the various other chores involved in operationalizing those models. The problem is that even a minor change in an algorithm can have a significant impact on the work…


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