AWS-MLDWTS - The Machine Learning Pipeline on AWS

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.

Student Testimonials

Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus. Student
Excellent presentation skills, subject matter knowledge, and command of the environment. Student
Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect. Student

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

Detailed Class Syllabus


Module 1: Introduction to Machine Learning and the ML Pipeline
Module 2: Introduction to Amazon SageMaker
Module 3: Problem Formulation
Module 4: Preprocessing
Module 5: Model Training
Module 6: Model Evaluation
Module 7: Feature Engineering and Model Tuning
Module 8: Deployment