Training > Cloud & Containers > Introduction to AI/ML Toolkits with Kubeflow (LFS147)
Training Course

Introduction to AI/ML Toolkits with Kubeflow (LFS147)

Explore the origins, deployment options, individual components and common integrations of Kubeflow.

Who Is It For

This course is designed for developers, engineers, data scientists or anyone interested in understanding the anatomy of a machine learning tool kit that harnesses the power of Kubernetes.
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What You’ll Learn

By the end of this course, you will understand Kubeflow’s architecture and key components and know how to prepare data, model training, serving, and management within Kubeflow.
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What It Prepares You For

You will be ready to deploy real-world ML projects using Kubeflow as well as have the skills and knowledge needed to contribute to the Kubeflow community.
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Course Outline
Chapter 1. The Model Application Relationship and the Power of Reproducibility
Chapter 2. The Model Development Lifecycle
Chapter 3. MLOPs and the Rise of the Machine Learning Toolkit
Chapter 4. The Origin of Kubeflow
Chapter 5. Kubeflow Distributions
Chapter 6. The Kubeflow Dashboard and Notebooks
Chapter 7. The Unified Training Operator and Machine Learning
Chapter 8. Kubeflow Pipelines
Chapter 9. Conquering Katib
Chapter 10. Common Kubeflow Integrations

To make the best of this course, you will need to have the following:

  • Experience with cloud computing
  • Familiarity with DevOps and cloud native principles 
  • Basic programming experience 
  • Experience with technical documentation
  • Experience with open source projects in general.
  • Basic understanding of Kubernetes might be helpful but not necessary