D8aland Mini Course: Amazon SageMaker Custom Configuration

Just enough overview of the technology with demos on how to create lifecycles scripts and custom JupyterLab images

What you’ll learn
What SageMaker is
The SageMaker and SageMaker Studio interface
The Studio JupyterLab environment
The training, inference, and deployment features in SageMaker
What the BASH shell is
Understand AWS IAM permissions and policies for SageMaker
What a SageMaker Lifecycle Script is and how it’s structured
Create and configure SageMaker Lifecycle configuration scripts
What SageMaker images are
What Docker is
The difference between containerization and virtual machines
What container repositories are
When to use a lifecycle script or a custom image
Best practices for creating and deploying lifecycle scripts and custom images
How to create a Docker container build environment with EC2 and ECR
Create and configure a SageMaker Custom Image

Requirements
Basic AWS familiarity is preferred but not required
Knowlege of BASH and Linux helpful but not required
Basic understanding of Docker helpful but not required
Knowledge of basic Python helpful but not required

Description
Do you need to customize a SageMaker environment but don’t have 40, 20, or even 10 hours to spend on training? I see plenty of other courses that teach how to create ML models in SageMaker, but NOT how to set it up and customize it.Introducing, d8aland’s new mini course on how to customize SageMakerOur mini courses are designed to be under two hours so you can get to the information you need quickly. They are created for specific use-cases instead of just a lengthy, high level tech overview. In other words, “how do I. . .?”For example: How do I . . .    Launch an AWS EC2 instance?    Build design and load an AWS RDS or Aurora database?    Create scheduled jobs with AWS Glue and Managed Apache Airflow?There are some explainer videos, but only enough to understand what we are doing in the demonstrations and why. In this case, how to create a Studio domain, launch JupyterLab and, configure lifecycle scripts and custom images.I hope you find great value in this course. Please shoot me an email if you have suggestions for this course or any other mini course you would like to see us make.Best of luck,Joe Cline

Who this course is for
Data scientist and analyst who build models in SageMaker but want to learn SageMaker customization
AWS Cloud Platform Enginners who wants to set up a custom SageMaker environment
Engineers who work on a cloud platform other than AWS and need to learn about the SageMaker service

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