Deploying Machine Learning models is known as the hardest problem in Data Science. Too many models live and die on a developers machine. We need a way to deploy our models in a repeatable way. In this session we will look at the basics and the history of Docker. We will build a Machine Learning model in Python, serialise it and containerise it.

Docker is great for packaging our applications, but we need somewhere to run it. For this we will use Kubernetes. Again we will look at the basics and history of K8s (how the kool kids write Kubernetes). We will then get our docker container running our model live and in to production.

Too few machine learning developers can deploy models, lets change this by running through all the examples together in this session.