The Machine Learning Pipeline on AWS.

This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises. This course will be delivered through a mix of instructor-led training (ILT) and hands-on labs

About the course

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.

This course is intended for

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 Prerequisites

Basic knowledge of Python programming language
Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
Basic experience working in a Jupyter notebook environment

This course is designed to teach you how to:

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 using 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

Agenda

Day One

Module 0: Introduction
Module 1: Introduction to Machine Learning and the ML Pipeline
Module 2: Introduction to Amazon SageMaker
Module 3: Problem Formulation

Day two and Three

Module 4: Preprocessing
Module 5: Model Training
Module 6: Model Evaluation

Day Four

Module 7: Feature Engineering and Model Tuning
Module 8: Deployment