Presentation: CI/CD for Machine Learning

I work on the Azure DevOps, and I'm focused on integrations between external products and our products. It's helping people automate everything and go with where most of the demand is. One of the things that we're working on is helping people who are working on machine learning to automate some of their deployments which is still an unsolved use case in most cases.

I want to show the ability to build a full continuous integration and continuous delivery pipeline for machine learning models, the ability to start from train the model, and get all the way to production, and also walk through what amount really is and how we can simplify that process.

If I had to summarize it in one line it would be any CI/CD pipeline is better than none. If you're going to automate major key pieces of this process will make your life a lot easier, simplify it and add speed to your deployments.

Models actually are not that static and for big companies like us or Google or Facebook they change a lot even on a daily basis, but for smaller companies what I see right now is that it takes people months to actually deploy the models to production so they can't even change the model even if they wanted to.

If you need to automate the training process, that piece can be filled by Azure Now. The market is still pretty young so there's just some tools coming up right now. Then you need the piece to put it all together in this CI/CD pipeline and deploy it, which is typically just deployment as an API endpoint, which you can consume as a service. For that you can use an automation tool such as, let's say Natural Pipelines or Jenkins. You can build those pieces out from the toolkits that are out there already, which allows you to get through from development to production in a couple of days instead of few months.