MLOps empowers data scientists and app developers to bring ML models to production. This session presents how to use MLOps in Azure to track, version, audit, certify and re-use every asset in your ML lifecycle and streamline the use of each resource. With practical examples on asset management and orchestration services for your ML model training and deployment workflows, you will learn about the Azure DevOps Machine Learning extension, best practices for data scientists to work in topic branches off master, when code is pushed to a Git repo, how to trigger a CI (continuous integration) pipeline, and how to provision ML workspaces, compute targets, datastores as infrastructure-as-code.