Machine Learning Pipeline On AWS

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment.

Students will learn about each phase of the processing 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. Learners with little to no machine learning

Audience Profile:

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

At Course Completion:

• Select and justify the appropriate ML approach for a given business proble
• 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