Course Outline


  • Kubeflow on Azure vs on-premise vs on other public cloud providers

Overview of Kubeflow Features and Architecture

Overview of the Deployment Process

Activating an Azure Account

Preparing and Launching GPU-enabled Virtual Machines

Setting up User Roles and Permissions

Preparing the Build Environment

Selecting a TensorFlow Model and Dataset

Packaging Code and Frameworks into a Docker Image

Setting up a Kubernetes Cluster Using AKS

Staging the Training and Validation Data

Configuring Kubeflow Pipelines

Launching a Training Job.

Visualizing the Training Job in Runtime

Cleaning up After the Job Completes


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 interested in machine learning model deployment.
  • Software engineers wishing to automate the integration and deployment of machine learning features with their application.
  28 Hours


Related Courses


  21 hours

Building Microservices with Microsoft Azure Service Fabric (ASF)

  21 hours

MLOps: CI/CD for Machine Learning

  35 hours

Kubeflow on AWS

  28 hours

Kubeflow on GCP

  28 hours

Kubeflow on IBM Cloud

  28 hours


  35 hours

Kubeflow on OpenShift

  28 hours

Kubeflow Fundamentals

  28 hours

Architecting Microsoft Azure Solutions

  14 hours

Introduction to Azure

  7 hours

Azure CLI: Getting Started

  7 hours