Course Outline

Introduction

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

Troubleshooting

Summary and Conclusion

Requirements

  • 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.

Audience

  • 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
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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