This course discusses how to use AWS services to train a model, deploy a model, and how to use AWS Lambda Functions, Step Functions to compose your model and services into an event-driven application.
Overview
Syllabus
- Introduction to Developing ML Workflows
- This lesson gives an introduction to the course, including prerequisites, final project, stakeholders, and tools & environment.
- SageMaker Essentials
- This lesson will go over SageMaker essential services such as training jobs, endpoints, batch transforms, and processing jobs.
- Designing Your First Workflow
- This lesson will discuss machine learning workflows and AWS tools such as Lambda, Step Function for building a workflow.
- Monitoring a ML Workflow
- This lesson will go over monitoring a machine learning workflow and some useful services within AWS to help you monitoring the healthy of data and machine learning models.
- Project: Build a ML Workflow For Scones Unlimited On Amazon SageMaker
- In the project, you will build and ship an image classification model with AWS SageMaker for Scones Unlimited, a scone-delivery-focused logistic company.
Taught by
Charles Landau and Joseph Nicolls