Opleiding: Kubeflow on Azure

Overview

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.

Requirements

• 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.

Course Outline

• 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

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Onze on line trainingen worden door een live instructeur verzorgd.

• Onze DaDesktop® -technologie creeert een digitale leeromgeving (en indien nodig een geclusterde enterprise infrastructuur) waarmee opdrachten en oefeningen uitgevoerd kunnen worden.
• De deelnemers (en de trainer) hebben toegang tot deze virtuele leeromgeving via de browser zodat hij/zij oefeningen kan doen die real time ingezien kunnen worden door de trainer.
• De trainer monitort niet alleen de voortgang van de prakitische oefeningen maar kan ook te hulp schieten en ingrijpen mocht dat nodig zijn.
• Onze remote trainingen verschillen niet van onze klassikale cursussen zowel qua duur, interactiviteit, praktische oefeningen als het cursusmateriaal.
• Door de flexibiliteit in de trainingsopbouw, de hoge mate van interactie tussen trainer en deelnemer en de hands-on oefeningen zijn onze onlinetrainingen zeer efficiënte en effectief.
• Ook onze in-company trainingen leveren wij on line met live instructeur.