The Machine Learning Pipeline on AWS

Learn how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.
AWS Training Partner
Private session

This training is available in a private or personalized format. It can be provided in one of our training centres or at your offices. Call one of our consultants of submit a request online.

Call now at 1 877 624.2344

  • Duration: 4 days
  • Regular price: On request

Course outline

Duration: 4 Days

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.

Audience

This course is intended for:
  • Developers
  • Solutions Architects
  • Data Engineers
  • Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon
  • SageMaker

Prerequisites

We recommend that attendees of this course have:
  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic experience working in a Jupyter notebook environment

Objectives

In this course, you will learn 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 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

Teaching method

Instructor-led training, hands-on labs, demonstrations, and group exercises

Contents

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 4: Preprocessing
  • Overview of data collection and integration, and techniques for data preprocessing and
  • visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about 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
  • Post-assessment
  • Course wrap-up

Surround yourself with the best

Frédéric Paradis
Frédéric Paradis
Certified Trainer and Cloud Architect
As a certified Microsoft trainer, Frédéric describes himself as a Cloud magician who easily navigates the mythical space between technology and reality.
Marc Maisonneuve
Marc Maisonneuve
Training program director
Marc Maisonneuve has acted as a Training Program Director, professional effectiveness trainer and user tools practice leader at AFI for several years. Mr. Maisonneuve is known for his analytical skills, his legendary calm and his undeniable desire to encourage people to develop their skills. He has the ability to present technological solutions in a natural way and to adapt them to the concrete needs of the workplace.
Vicky Moreau
Vicky Moreau
Trainer
Vicky Moreau is a passionate freelancer and professional in the area of office automation. She holds a college diploma in office automation, most of her solid experience with the Office Suite was acquired while being an autodidact. In fact, she has successfully completed an MOS (Microsoft Office Specialist) Excel certification.
Francis Ferland-Stevenson
Francis Ferland-Stevenson
Trainer
Francis began as a trainer more than 5 years ago by testing office automation tools designed specifically to met the needs of his colleagues. His calm and his empathy makes him able to adapt his language according to the level of experience of the group. This makes his learnings clear and accessible to anyone. As a trainer, he is therefore attentive to the needs of his students to make sure they meet their objectives and face the challenges.