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


  • 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.
  28 Hours


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