Getting started with Azure Machine Learning

Not so long ago, if you wanted to use ML in a production environment on the Azure platform, you needed to bring together a bunch of different services to support the full ML life cycle.

For example, to use the datasets, you would need storage repositories such as Azure Blob storage or Azure Data Lake storage. For compute, you would either need individual virtual machines, Spark clusters using HDInsight, or Azure Databricks to actually run your model code. To protect your data for enterprise readiness, you'd need to bring in your virtual networks or configure your compute and data inside the same virtual network, along with Azure Key Vault to manage and secure your credentials. In order to provide repeatability for your experiments by using a consistent set of ML libraries, and the different versions thereof, you'd create Docker containers and use Azure Container Registry to store those Docker containers. You would need to put...