Machine Learning Pipeline on Amazon 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 you’ll 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. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays. This 4 day, 32 hour class prepares those pursuing both the AWS Certified Machine Learning Engineer - Associate (Exam MLA - C01) certifications.
About this Course
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
Outline
Day 1 Module 0: Introduction Module 1: Introduction to Machine Learning and the ML Pipeline Module 2: Introduction to Amazon SageMaker Module 3: Problem Formulation
Day 2 Module 3: Problem Formulation (continued) Module 4: Preprocessing
Day 3 Module 5: Model Training Module 6: Model Evaluation
Day 4 Module 7: Feature Engineering and Model Tuning Module 8: Deployment
Exam Details
AWS Exam: MLA - C01 - AWS Certified Machine Learning Engineer - Associate
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