Apache Spark 2.0 Preview: Machine Learning Model Persistence

Spark Summit 2016 will be held in San Francisco on June 6–8. Check out the full agenda and get your ticket before it sells out!Introduction Consider these Machine Learning (ML) use cases: A data scientist produces an ML model and hands it over to an engineering team for deployment in a production environment. A data engineer integrates a model training workflow in Python with a model serving workflow in Java. A data scientist creates jobs to train many ML models, to be saved and evaluated later. All of these use cases are easier with model persistence, the ability to save and load models. With the upcoming release of Apache Spark 2.0, Spark’s Machine Learning library MLlib will include near-complete support for ML persistence in the DataFrame-based API. This blog post…

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