Kubeflow on AWS Training Course

Kubeflow serves as a framework designed for executing Machine Learning workloads on Kubernetes. While TensorFlow is a library dedicated to machine learning, Kubernetes acts as the orchestration platform responsible for managing containerized applications.

This instructor-led training, available either online or onsite, is specifically targeted at engineers who aim to deploy Machine Learning workloads onto an AWS EC2 server.

Upon completing this training, participants will be capable of:

Installing and configuring Kubernetes, Kubeflow, and other required software within the AWS environment.
Utilizing EKS (Elastic Kubernetes Service) to streamline the initialization of a Kubernetes cluster on AWS.
Establishing and deploying a Kubernetes pipeline to automate and manage ML models in production environments.
Training and deploying TensorFlow ML models across multiple GPUs and machines operating in parallel.
Utilizing other AWS managed services to expand the capabilities of an ML application.

Course Format

Interactive lectures and discussions.
Extensive exercises and practical sessions.
Practical implementation within a live-lab environment.

Course Customization Options

For those requesting customized training for this course, please contact us to make arrangements.

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

Thank you for sending your enquiry! One of our team members will contact you shortly.

Thank you for sending your booking! One of our team members will contact you shortly.

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

A solid understanding of machine learning concepts.
Knowledge of cloud computing principles.
A general familiarity with containers (Docker) and orchestration (Kubernetes).
Some prior experience with Python programming is beneficial.
Experience in navigating the command line.

Target Audience

Data science engineers.
DevOps engineers interested in the deployment of machine learning models.
Infrastructure engineers interested in the deployment of machine learning models.
Software engineers seeking to integrate and deploy machine learning features into their applications.

28 Hours

Custom Corporate Training

Training solutions designed exclusively for businesses.

Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
Flexible Schedule: Dates and times adapted to your team's agenda.
Format: Online (live), In-company (at your offices), or Hybrid.

Investment

Price per private group, online live training, starting from 5200 € + VAT*

(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)

Need help picking the right course?

Testimonials (3)

I've find out new interesting things about Lambda and Serverless

Oleg Buldumac - PUBLIC COURSE

Course - AWS Lambda for Developers

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

Guillaume GAUTIER - OLEA MEDICAL

Course - MLflow

I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.

Kubeflow on AWS Training Course

Course Outline