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.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

By the end of this course, you 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
Apply machine learning to a real-life business problem after the course is complete

  • Module 0- Introduction

  • Pre-assessment

  • Module 1- Introduction to Machine Learning and the ML Pipeline

  • Overview of machine learning, including use cases, types of machine learning, and key concepts
    Overview of the ML pipeline
    Introduction to course projects and approach

  • Module 2- Introduction to Amazon SageMaker

  • Introduction to Amazon SageMaker
    Demo- Amazon SageMaker and Jupyter notebooks
    Hands-on- Amazon SageMaker and Jupyter notebooks

  • Module 3- Problem Formulation

  • Overview of problem formulation and deciding if ML is the right solution
    Converting a business problem into an ML problem
    Demo- Amazon SageMaker Ground Truth
    Hands-on- Amazon SageMaker Ground Truth
    Practice problem formulation
    Formulate problems for projects

  • Module 5- Model Training

  • Choosing the right algorithm
    Formatting and splitting your data for training
    Loss functions and gradient descent for improving your model
    Demo- Create a training job in Amazon SageMaker

  • Module 6- Model Evaluation

  • How to evaluate classification models
    How to evaluate regression models
    Practice model training and evaluation
    Train and evaluate project models
    Initial project presentations

  • Module 7- Feature Engineering and Model Tuning

  • Feature extraction, selection, creation, and transformation
    Hyperparameter tuning
    Demo- SageMaker hyperparameter optimization
    Practice feature engineering and model tuning
    Apply feature engineering and model tuning to projects
    Final project presentations

  • Module 8- Deployment

  • How to deploy, inference, and monitor your model on Amazon SageMaker
    Deploying ML at the edge
    Demo- Creating an Amazon SageMaker endpoint
    Course wrap-up


This course is best for those whose job role could be 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.




Basic knowledge of Python Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch) Basic understanding of working in a Jupyter notebook environment


Length: 4.0 days (32 hours)


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