Kubeflow on OpenShift Training Course


28 hours (usually 4 days including breaks)


  • 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.


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


Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is a cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.

  • By the end of this training, participants will be able to:
  • Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
  • Use OpenShift to simplify the work of initializing a Kubernetes cluster.
  • Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
  • Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
  • Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.

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.

Course Outline


  • Kubeflow on OpenShift vs public cloud managed services

Overview of Kubeflow on OpenShift

  • Code Read Containers
  • Storage options

Overview of Environment Setup

  • Setting up a Kubernetes cluster

Setting up Kubeflow on OpenShift

  • Installing Kubeflow

Coding the Model

  • Choosing an ML algorithm
  • Implementing a TensorFlow CNN model

Reading the Data

  • Accessing a dataset

Kubeflow Pipelines on OpenShift

  • Setting up an end-to-end Kubeflow pipeline
  • Customizing Kubeflow Pipelines

Running an ML Training Job

  • Training a model

Deploying the Model

  • Running a trained model on OpenShift

Integrating the Model into a Web Application

  • Creating a sample application
  • Sending prediction requests

Administering Kubeflow

  • Monitoring with Tensorboard
  • Managing logs

Securing a Kubeflow Cluster

  • Setting up authentication and authorization


Summary and Conclusion

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