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Course Outline

Introduction

  • Kubeflow on AWS compared to on-premise setups and other public cloud providers

Overview of Kubeflow Features and Architecture

Activating an AWS Account

Preparing and Launching GPU-enabled AWS Instances

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 EKS

Staging the Training and Validation Data

Configuring Kubeflow Pipelines

Launching a Training Job using Kubeflow in EKS

Visualizing the Training Job in Runtime

Cleaning Up After the Job Completes

Troubleshooting

Summary and Conclusion

Requirements

  • A fundamental understanding of machine learning concepts.
  • Knowledge of cloud computing principles.
  • A general comprehension of containers (Docker) and orchestration (Kubernetes).
  • Some experience with Python programming is advantageous.
  • Experience working with a command-line interface.

Target Audience

  • Data science engineers.
  • DevOps engineers interested in machine learning model deployment.
  • Infrastructure engineers interested in machine learning model deployment.
  • Software engineers aiming to integrate and deploy machine learning features within their applications.
 28 Hours

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