Machine Learning at Scale with Databricks and Kubernetes

Machine Learning Operationalisation (ML Ops) is a set of practices that aim to quickly and reliably build, deploy and monitor machine learning applications. Many organizations standardize around certain tools to develop a platform to enable these goals. One combination of tools includes using Databricks to build and manage machine learning models and Kubernetes to deploy models. This article will explore how to design this solution on Microsoft Azure followed by step-by-step instructions on how to implement this solution as a proof-of-concept. This approach aims to use common open source technologies and can easily be adapted for other cloud platforms.