Machine Learning Pipelines on AWS.

About This Course

Course Code

Course Type


4 Days


Course Overview

Special Notices

In order to facilitate your AWS e-courseware and lab provision Nexus will need to share some of your data. For more information please view our Nexus Partner Data Sharing Statement. If you have any questions or concerns please contact your Nexus account manager.


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

This course is intended for:

Delivery Method

This course is delivered through a mix of:


In this course, you will learn how to:

Course Outline

Module 1: Introduction to Machine Learning and the ML Pipeline

Module 2: Introduction to Amazon SageMaker

Module 3: Problem Formulation

Day Two

Recap and Checkpoint #1

Module 4: Preprocessing

Module 5: Model Training

Day Three

Recap and Checkpoint #2

Module 6: Model Training

Module 7: Feature Engineering and Model Tuning

Day Four

Lab 4: Feature Engineering (including project work)

Recap and Checkpoint #3

Module 8: Module Deployment

Module 9: Course Wrap-Up


We recommend that attendees of this course have the following prerequisites:

Dates & Prices

Monday 6th September 2021


Monday 25th October 2021


Monday 6th December 2021