Overview
Provide solutions for actual business challenges with AWS machine learning.
In a project-based learning setting, the Machine Learning Pipeline on AWS course demonstrates how to apply the machine learning (ML) pipeline to resolve actual business issues. Through presentations and demonstrations by the teacher, students will gain information about every stage of the pipeline. They will then use this knowledge to solve a project that addresses one of three business problems: recommendation engines, fraud detection, or airline delays. By the end of the course, students will have used Amazon SageMaker to successfully build, train, analyze, tune, and deploy an ML model that addresses the business challenge of their choice.
Note: Lab time is only accessible during class; it cannot be used after that. There are extra lab fees for "repeat students."
- Select and justify the appropriate ML approach for a given business problem
- 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
- Cloud Developer
- Software Developer
- AWS Architect
- Data Engineer
Required
Basic experience working in a Jupyter notebook environment
Basic knowledge of Python programming language
Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)