Description

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

Intended Audience 

• Developers
• Solutions Architects
• Data Engineers
• Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Course Objectives

• Select and justify the appropriate ML approach for a given business problem
• Use the ML pipeline to solve a specific business problem
• Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
• Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
• Apply machine learning to a real-life business problem after the course is complete

Course Outline

Day 1

• Introduction to Machine Learning and the ML Pipeline
• Introduction to Amazon SageMaker
• Problem Formulation

Day 2

• Pre-processing
• Model Training

Day 3

• Model Training
• Feature Engineering and Model Tuning

Day 4

• Lab 4: Feature Engineering (including project work)
• Module Deployment