The Machine Learning Pipeline on AWS

Live Online (VILT) & Classroom Corporate Training Course

Aws Certified

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.

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The Machine Learning Pipeline on AWS

Overview

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process 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. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.

Activites

This course includes presentations, group exercises, demonstrations, and hands-on labs.

Objectives

In this Data Warehousing on AWS course, participants 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 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

Prerequisites

We recommend that attendees of The Machine Learning Pipeline on AWS 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

Course Outline

Module 02022-12-21T11:40:52+05:30

Introduction

  • Pre-assessment
Module 12022-12-21T11:41:45+05:30

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 22022-12-21T11:42:36+05:30

Introduction to Amazon SageMaker

  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Hands-on: Amazon SageMaker and Jupyter notebooks
Module 32022-12-21T11:43:33+05:30

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
Checkpoint 12022-12-21T11:44:28+05:30

Checkpoint 1 and Answer Review

Module 42022-12-21T11:46:04+05:30

Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about projects
Checkpoint 22022-12-21T11:46:44+05:30

Checkpoint 2 and Answer Review

Module 52022-12-21T11:47:54+05:30

Model Training

  • Choosing the right algorithm
  • Formatting and splitting your data for training
  • Loss functions and gradient descent for improving your model
  • The Machine Learning Pipeline on AWS
  • AWS Classroom Training
  • Demo: Create a training job in Amazon SageMaker
Module 62022-12-21T11:48:59+05:30

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
Checkpoint 32022-12-21T11:49:41+05:30

Checkpoint 3 and Answer Review

Module 72022-12-21T11:50:58+05:30

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 82022-12-21T11:51:55+05:30

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

AWS Discovery Days

Supercharge your workforce’s AWS skills with our complimentary Privately Hosted AWS Discovery Day. Delivered by our team of renowned AWS Authorized Instructors, this tailored experience will propel your organization’s technological capabilities to new heights.

Request AWS Discovery Day
2023-12-05T17:50:57+05:30

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