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.
Who should attend
- Solutions Architects
- Data Engineers
- Anyone Who Wants to Learn About the ML Pipeline via Amazon SageMaker (even if they have little to no experience with machine learning)
- Basic knowledge of Python.
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch).
- Basic understanding of working in a Jupyter notebook environment.
Outline: The Machine Learning Pipeline on AWS (AWS-MLDWTS)
- 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