Course Outline

Introduction

  • Kubeflow on Azure vs on-premise vs on other public cloud providers

Overview of Kubeflow Features and Architecture

Overview of the Deployment Process

Activating an Azure Account

Preparing and Launching GPU-enabled Virtual Machines

Setting up User Roles and Permissions

Preparing the Build Environment

Selecting a TensorFlow Model and Dataset

Packaging Code and Frameworks into a Docker Image

Setting up a Kubernetes Cluster Using AKS

Staging the Training and Validation Data

Configuring Kubeflow Pipelines

Launching a Training Job.

Visualizing the Training Job in Runtime

Cleaning up After the Job Completes

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).
  • Some Python programming experience is helpful.
  • Experience working with a command line.

Audience

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

Number of participants


Starts

Ends


Dates are subject to availability and take place between 9:30 am and 4:30 pm.
Open Training Courses require 5+ participants.

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