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The Machine Learning Pipeline on AWS

SS Course: 58618

Course Overview

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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.                                                                  

Scheduled Classes

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12/19/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
01/24/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
02/13/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
03/14/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
04/03/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
04/25/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
05/23/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
06/06/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
06/26/23 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

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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

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Viewing outline for:
Module 0: Introduction
  • Module 1: Introduction to Machine Learning and the ML Pipeline
  • Module 2: Introduction to Amazon SageMaker
  • Module 3: Problem Formulation
  • Module 3: Problem Formulation (continued)
  • Module 4: Preprocessing
  • Module 5: Model Training
  • Module 6: Model Evaluation
  • Module 7: Feature Engineering and Model Tuning
  • Module 8: Deployment

Prerequisites

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Basic knowledge of Python

    Who Should Attend

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    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

    Next Step Courses

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