The Machine Learning Pipeline on AWS

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

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

Day One

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 

Day Two

Checkpoint 1 and Answer Review

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 
  

Day Three               

Checkpoint 2 and Answer Review

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 

Day Four

Checkpoint 3 and Answer Review

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

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

• GK4534 - AWS Cloud Practitioner Essentials