Making Sense of Big Data, Notes from Industry

Virtualization for Machine Learning

Knowing how to host Machine Learning (ML) applications, e.g., training/testing pipelines, batch/streaming prediction jobs, business-focused analytics applications, etc., is an essential skill dimension for a machine learning engineer. Operationalizing ML models have many different deployment possibilities. Reproducible ML environments were a challenging problem to solve just a few years ago. Virtualization on a declarative public cloud platform is exceptionally relevant in that case…