Course Outline


Overview of Azure Machine Learning (AML) Features and Architecture

Overview of an End-to-End Workflow in AML (Azure Machine Learning Pipelines)

Provisioning Virtual Machines in the Cloud

Scaling Considerations (CPUs, GPUs, and FPGAs)

Navigating Azure Machine Learning Studio

Preparing Data

Building a Model

Training and Testing a Model

Registering a Trained Model

Building a Model Image

Deploying a Model

Monitoring a Model in Production


Summary and Conclusion


  • An understanding of machine learning concepts.
  • Knowledge of cloud computing concepts.
  • A general understanding of containers (Docker) and orchestration (Kubernetes).
  • Python or R programming experience is helpful.
  • Experience working with a command line.


  • Data science engineers
  • DevOps engineers interested in machine learning model deployment
  • Infrastructure engineers interesting in machine learning model deployment
  • Software engineers wishing to automate the integration and deployment of machine learning features with their application
  21 Hours


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