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 youll 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. Youll have a choice of projects: fraud detection, recommendation engines, or flight delays.                                                                  

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 in 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

Basic knowledge of Python

• Basic understanding of working in a Jupyter notebook environment
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• Take this skills assessment to see if you have the required knowledge to skip AWS Technical Essentials, the first course in the AWS technical training path: https://www.exitcertified.com/training-resources/skills-assessments/aws-skills-assessment

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