Kubeflow on Azure

• 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.

Audience

• 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.

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.

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

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

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• 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.
• Leverage other AWS managed services 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.

Introduction

• 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

Troubleshooting

Summary and Conclusion