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The Machine Learning Pipeline on AWS

Overview

The Amazon Web Services (AWS) authorized Machine Learning Pipeline on AWS course explores how to use an iterative machine learning process pipeline to solve real business problems in a project-based learning environment. In this course, participants will learn about each phase of the process pipeline from instructor presentations and demonstrations, and then apply the knowledge to complete a project solving one of three business problems – fraud detection, recommended engines, or flight delays. By the end of the course, participants would be able to successfully build, train, evaluate, tune, and deploy machine learning models using Amazon SageMaker that solves their selected business problem.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 4 days

Live Virtual Classroom
Duration: 4 days

What You'll learn

  • Select and justify the appropriate machine learning approach for a given business problem
  • Use the machine learning pipeline to solve a specific business problem
  • Train, evaluate, deploy, and tune a machine learning model using 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

  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the machine learning pipeline
  • Introduction to course projects and approach
  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Hands-on: Amazon SageMaker and Jupyter notebooks
  • Overview of problem formulation & 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
  • Overview of data collection and integration, and techniques for data processing and visualization
  • Practice pre-processing
  • Pre-processing project data
  • Class discussion about the projects
  • 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
  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Initial project presentations
  • 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
  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying machine learning at the edge
  • Demo: Creating an Amazon SageMaker endpoint
  • Post-assessment
  • Course wrap-up
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Prerequisites

To attend this course, participants need to have –

  • Basic knowledge of Python programming language
  • Basic understanding of Amazon Cloud Infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic experience working in a Jupyter notebook environment

Who Should Attend

The course is intended for researchers and IT professionals interested in getting an introduction to machine learning using Python and Amazon SageMaker

Interested in this course? Let’s connect!