crupley/aws-immersion-ml-public

Machine Learning Immersion Day

AWS Machine Learning Workshop

This is a set of instructions and exercises for a Machine Learning Workshop. This Machine Learning oriented content is focused on the use of Kubernetes (i.e. EKS).

Overview

These instructions assume your Workshop is using Event Engine. You will have the following resources pre-configured in us-west-2 (Oregon) region:

• EKS Cluster named kf-sm-workshop
• Sagemaker Notebook with AWS CLI, eksctl, kubectl, aws-iam-authentictor, git, and kfctl.

Note: if you are running this workshop on your own, please see the Self Paced Instructions (Note: as of April 30, these instructions are still in construction and may not work properly).

First Steps

1. Login to your AWS Account using the supplied method.
2. Navigate to SageMaker Service
3. Verify / Change to the Oregon (us-west-2) region
4. Launch Juypter (or Juypter Hub) on the BasicNotebookInstance
5. Open a terminal and switch to 'bash' by typing bash at the terminal prompt
6. Run the command: eksctl get clusters - you should see the following:

   NAME            REGION
   kf-sm-workshop  us-west-2
   

7. Run the command: aws eks update-kubeconfig --name kf-sm-workshop
8. Confirm connectivity to EKS by running kubectl get nodes -A - you should see a list of six nodes.

What's Next

If you want to follow along in a different browser, navigate to The Source Github project.

There are several labs included with this Workshop, including:

• Kubeflow on EKS
• Kubeflow Pipelines with SageMaker
• SageMaker Operators for Kubernetes
• SageMaker Build Train Deploy
• SageMaker Hyper-Parameter Optimization
• SageMaker Batch Transform
• SageMaker Multi-Model Endpoints (MME)