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