Kubeflow on IBM Cloud

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 IBM Cloud Kubernetes Service (IKS).

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

• Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
• Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
• 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 IBM Cloud 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 interesting 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 IKS vs on-premise vs on other public cloud providers

Overview of Kubeflow Features on IBM Cloud

• IKS
• IBM Cloud Object Storage

Overview of Environment Setup

• Preparing virtual machines
• Setting up a Kubernetes cluster

Setting up Kubeflow on IBM Cloud

• Installing Kubeflow through IKS

Coding the Model

• Choosing an ML algorithm
• Implementing a TensorFlow CNN model

Reading the Data

• Accessing the MNIST dataset

Pipelines on IBM Cloud

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

Running an ML Training Job

• Training an MNIST model

Deploying the Model

• Running TensorFlow Serving on IKS

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

Troubleshooting

Summary and Conclusion

.

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.