DevOps practices range from continuous integration to continuous delivery from ensuring production is always online or easy to rollback to measuring all the things, from ensuring code and knowledge get shared to having a loosely coupled architecture and of course much more.

How do we actually get that to work with Machine Learning? And why should you care?
First of all, it turns out that any (non-) data scientist can actually be of value in the lifecycle of a machine learning solution. But more importantly, it turns out that by using the knowledge and experience from the community, it's actually possible to transform the way you deliver your machine learning solutions to the end user. It turns out it's even possible to do this much faster than ever before.

In this session, you'll be guided through a solution that uses DevOps practices to help you overcome classic issues with delivering machine learning solutions. Come for the buzzwords, stay because this is what you've always wanted in your organisation!
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