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Building End-to-end Machine Learning Workflows with Kubeflow 1

سرفصل های دوره

In this course, you will learn how to effectively build end-to-end scalable, production grade machine learning workflows using Kubeflow.


1. Course Overview
  • 1. Course Overview

  • 2. Introduction
  • 1. Version Check
  • 2. Course Introduction
  • 3. Kubeflow Overview
  • 4. Course Structure
  • 5. Fashion-MNIST Use Case Overview

  • 3. Setting up Kubeflow Environment
  • 1. Introduction
  • 2. Overview
  • 3. Kubeflow Deployment Options
  • 4. Setup Kubeflow on GCP
  • 5. Demo - Setup Kubeflow Prerequisites
  • 6. Demo - Setup OAuth for GCP Cloud IAP
  • 7. Demo - Setup Kubeflow on GCP
  • 8. Demo - Clean Kubeflow Environment on GCP
  • 9. Summary

  • 04. Exploring Kubeflow Components
  • 01. Introduction
  • 02. Overview
  • 03. Docker Introduction
  • 04. Demo - Docker Overview
  • 05. Why Kubernetes
  • 06. Kubernetes Introduction
  • 07. Kubernetes Core Components
  • 08. Demo - Kubernetes Overview (Part 1)
  • 09. Demo - Kubeflow Overview (Part 2)
  • 10. Demo - Kubeflow Central Dashboard Overview
  • 11. Summary

  • 05. Building Machine Learning Model on Kubeflow
  • 01. Introduction
  • 02. Overview
  • 03. Model Development Process and Challenges
  • 04. Kubeflow Components for Training
  • 05. Fashion-MNIST Training Workflow
  • 06. Kubeflow Notebook
  • 07. Demo - Setting up Notebook Server with a Pre-built Image
  • 08. Demo - Setting up Notebook Server with a Custom Image
  • 09. Deep Learning Model Overview for Fashion-MNIST
  • 10. Demo - Training in Kubeflow Notebook
  • 11. Metadata Overview
  • 12. Demo - Metadata Tracking
  • 13. Kubeflow Fairing Overview
  • 14. Demo - Kubeflow Fairing
  • 15. Distributed Training
  • 16. Demo - Distributed Training with GPU
  • 17. Demo - Distributed Training with TFJob
  • 18. Hyperparameter Tuning with Katib
  • 19. Demo - Performing Hyperparameter Tuning with Katib
  • 20. Summary

  • 06. Serving Machine Learning Model on Kubeflow
  • 01. Introduction
  • 02. Overview
  • 03. Model Serving Process and Challenges
  • 04. Kubeflow Components for Serving
  • 05. KFServing Overview
  • 06. Demo - Serving Model Using KFServing
  • 07. Demo - Pre and Post-processing Using KFServing
  • 08. Canary Rollout Overview
  • 09. Demo - Canary Rollout Using KFServing
  • 10. Demo - Performance Monitoring Using KFServing, Prometheus, and
  • 11. Demo - Auto Scaling and Load Testing
  • 12. Summary

  • 07. Build Machine Learning Pipeline Using Kubeflow Pipeline
  • 01. Introduction
  • 02. Overview
  • 03. Machine Learning Workflow Pipeline and Challenges
  • 04. Kubeflow Components for Building Pipeline
  • 05. Kubeflow Pipeline Overview
  • 06. Fashion-MNIST Use Case Pipeline
  • 07. Demo - Building Kubeflow Pipeline with Hyperparam
  • 08. Demo - Adding Training Step to Kubeflow Pipeline
  • 09. Demo - Adding Serving Step to Kubeflow Pipeline
  • 10. Demo - Building Kubeflow Pipeline from Notebook
  • 11. Summary

  • 8. Whats Next
  • 1. Introduction
  • 2. Extend Your Kubeflow Journey
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