The Machine Learning Pipeline On AWS

by NTUC LearningHub Claim Listing

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

$3672

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img Duration

32 Hours

Course Details

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.

They will 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.

 

Pre-requisites

Upon Completing This Course, Participants Will Be Able To:

  • 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

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