Azure Machine Learning services eliminated the need for data scientists to install and operate servers for data storage, extraction and model execution.
Microsoft introduced three new machine learning services on Azure: Workbench, Experimentation and Model Management.
Workbench is a data science workflow consists of model development, experimentation and tuning, which then culminate in model deployment and management.
Experimentation is the control plane for machine learning model training runs that facilitate execution on a local computer, a local Docker container, a remote compute instance or container, or an Apache Spark cluster.
The service works with Workbench projects and supports other features, including Git integration, access controls, project roaming and sharing.
Model Management registers and tracks the various training runs and manages the results with model versions and forks.
