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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.
Testimonials
I genuinely liked the new technology.
- PCCW
examples, preparation of materials, level of knowledge of trainer, excellent communication
Michał Krasucki - Instytut Lotnictwa
Practice parts
- Instytut Lotnictwa
The training is practical and it is good for understanding how to use AWS step by step
- PCCW
That it was all new technology and offerings to myself. After being shown how quick and easy it is to set up certain services in AWS, I feel I could get a real benefit out of it for quick project and proposal prototyping.
MDA Systems Ltd.
Fernando knew the products and how to use them. His willingness and friendliness to assist in the hands-on lab was great.
MDA Systems Ltd.
There was a good general pass over what seemed like the most important parts of AWS. The instructor was open to questions and addressed areas of AWS that were not part of the outline based on our questions.
MDA Systems Ltd.
I liked getting to understand the breadth of the services offered by AWS and gaining a better understanding of their pricing model for each of those services.
William Crowdis - MDA Systems Ltd.
Thought it was a good overview of a lot of different services. Liked the detail on IAS.
MDA Systems Ltd.
Explaining why it's financially viable to do these things
MDA Systems Ltd.
It provided context for the things we do in AWS.
MDA Systems Ltd.
Everything. I had played around with AWS before but just on my own personal time. The training really brought everything together, with real world examples and how many individual pieces can be bolted together for a applicable solution.
Matt Sartain - MDA Systems Ltd.
Hands-on labs
MDA Systems Ltd.
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