Kubeflow Fundamentals Training Course


Kubeflow is a toolkit for making Machine Learning (ML) on Kubernetes easy, portable and scalable.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

  • Install and configure Kubeflow on premise and in the cloud.
  • Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
  • To learn more about Kubeflow, please visit: https://github.com/kubeflow/kubeflow


  • Familiarity with Python syntax
  • Experience with Tensorflow, PyTorch, or other machine learning framework
  • A public cloud provider account (optional)


  • Developers
  • Data scientists

Course Outline


Overview of Kubeflow Features and Components

  • Containers, manifests, etc.

Overview of a Machine Learning Pipeline

  • Training, testing, tuning, deploying, etc.

Deploying Kubeflow to a Kubernetes Cluster

  • Preparing the execution environment (training cluster, production cluster, etc.)
  • Downloading, installing and customizing.

Running a Machine Learning Pipeline on Kubernetes

  • Building a TensorFlow pipeline.
  • Building a PyTorch pipleline.

Visualizing the Results

  • Exporting and visualizing pipeline metrics

Customizing the Execution Environment

  • Customizing the stack for diverse infrastructures
  • Upgrading a Kubeflow deployment

Running Kubeflow on Public Clouds

  • AWS, Microsoft Azure, Google Cloud Platform

Managing Production Workflows

  • Running with GitOps methodology
  • Scheduling jobs
  • Spawning Jupyter notebooks


Summary and Conclusion

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