The Machine Learning Pipeline on AWS

Program Code: 3031

Overview
Schedule classes
What you'll learn
Outline
Prerequisites
Who should attend

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

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 machine learning pipeline
Introduction to course projects and approach

Module 2: Introduction to the 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 & 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: Pre-processing

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

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 machine learning at the edge
Demo: Creating an Amazon SageMaker endpoint
Post-assessment
Course wrap-up

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