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The Machine Learning Pipeline on AWS (AWS-MLDWTS)


Course Overview

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

  • Developers
  • 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
Online Training

Duration 4 days

  • US$ 2,700
Classroom Training

Duration 4 days

  • United States: US$ 2,700
Click on town name or "Online Training" to book Schedule
This is an Instructor-Led Classroom course
Instructor-led Online Training:   This is an Instructor-Led Online (ILO) course. These sessions are conducted via WebEx in a VoIP environment and require an Internet Connection and headset with microphone connected to your computer or laptop.
  *   This class is delivered by a partner.
United States
Online Training 08:30 US/Central * Enroll
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