Productionizing Machine Learning with a Microservices Architecture

Deploying machine learning models from training to production requires companies to deal with the complexity of moving workloads through different pipelines and re-writing code from scratch. Yaron Haviv will explain how to automatically transfer machine learning models to production by running Spark as a microservice for inferencing, achieving auto-scaling, versioning and security. He will demonstrate how to feed feature vectors aggregated from multivariate real-time and historical data to machine learning