Kubeflow on AWS Training Course

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is a machine learning library and 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 an AWS EC2 server.

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

Install and configure Kubernetes, Kubeflow and other needed software on AWS.
Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
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.

This course is available as onsite live training in Netherlands or online live training.

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Course Outline

Introduction

Kubeflow on AWS vs on-premise vs on other public cloud providers

Overview of Kubeflow Features and Architecture

Activating an AWS Account

Preparing and Launching GPU-enabled AWS Instances

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 EKS

Staging the Training and Validation Data

Configuring Kubeflow Pipelines

Launching a Training Job using Kubeflow in EKS

Visualizing the Training Job in Runtime

Cleaning up After the Job Completes

Troubleshooting

Summary and Conclusion

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 integrate and deploy machine learning features with their application.

28 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €9120 online delivery, based on a group of 2 delegates, €2880 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

Public Training

Please see our public courses

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Testimonials (3)

the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose

Guillaume GAUTIER - OLEA MEDICAL

Course - MLflow

All good, nothing to improve

Ievgen Vinchyk - GE Medical Systems Polska Sp. Z O.O.

Course - AWS Lambda for Developers

IOT applications