The overall workflow for Azure Machine Learning, shown in the figure below, moves from data preparation to model building, to training and testing, to model management and deployment.

Azure ML Model Management Service The Model Management service provides customers with the control and flexibility of where and how they want to deploy their models.

Wallaroo’s model operations platform for deploying, observing, and managing machine learning in production enables customers to easily operationalize ML Models with our purpose-built inference ...

The new interface for Azure’s automated machine learning tool makes creating a model as easy as importing a data set and then telling the service which value to predict.

And Azure ML Integration with mlflow, an open source platform for AI lifecycle management, now includes support for submitting jobs to the cloud, model registry and deployment support, and ...

Azure ML Experimentation service is built supporting collaborative model development at scale. It uses Git repositories and a command-line tool to manage model experimentation and training.