The Machine Learning Pipeline on AWS

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.

This intermediate-level course is delivered through a mix of instructor-led training (ILT), hands-on labs, demonstrations, and group exercises.

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 designed to teach you how to:

• Select an appropriate ML approach for a business problem
• How to use the ML pipeline to solve a specific business problem
• How to train, evaluate, deploy, and tune an ML model in Amazon SageMaker
• The best practices for designing scalable, cost-optimised, and secure ML pipelines in AWS
• How to apply ML to a real-life problem in your business

This course is intended for:

• Developers
• Solutions Architects
• Data Engineers
• Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning.

We recommend that attendees of this course have:

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