Awesome, your shiny new machine learning model has been trained, now what?

How do you manage its lifecycle, and make sure that both the model and its data source are versioned so that they can be audited? How do you handle the infrastructure needed to serve your model, and wouldn't it also be nice if the model training, evaluation, and deployment all where automated steps as soon as a change was checked in?

Join me in this session where we will take a deep dive in to MLOps, DevOps for Machine Learning. We'll look at how we can leverage tools such as Azure Machine Learning Service and Azure DevOps to set up automated pipelines for your machine learning environment.