Get started with Machine Learning and Auto ML using Azure ML Studio easily building powerful models and deploy them in a scalable environment.

Everything you need to know to get up and running quickly in Machine Learning using Azure, from datasets to containerized deployments and data validation.

Topics include:

* An introduction to Azure ML Studio and AutoML

* Import datasets and version them within Azure - even from remote locations like Github

* Create compute clusters for training and live inferencing

* Select models for deployment into Kubernetes or Docker Container instances

* Practice MLOps, operationalizing a deployed model with logs and metrics via Application Insights

* Consume a live endpoint with a deployed model over an HTTP API

* Use the generated Swagger files to grasp the HTTP endpoints