Automation has long been a corner stone of modern Software Engineering and has given rise to successful initiatives such as DevOps.

Automating the end-to-end lifecycle of a machine learning model, from training to deployment in production, is a lot more complex and requires careful consideration of multiple axis of change, such as data, code and configuration, but can yield fantastic results when done right.

MLOps, also known as Continuous Delivery for Machine Learning (CD4ML), has recently gotten a lot of traction and was mentioned by ThoughtWorks as a technique worth keeping a close eye on (https://www.thoughtworks.com/radar/techniques)

Join me in this session where we’ll walk through some common concepts related to MLOps, and look at tools and techniques to achieve this both on-premise and in the cloud.