Day 1
Session 1: Introduction to AWS SageMaker and Machine Learning Basics

• Overview of AWS SageMaker and its ecosystem
• Key concepts of machine learning and its applications
• Hands-on lab: Setting up SageMaker and navigating its interface

Session 2: Preparing Data for Machine Learning in SageMaker

• Importing and preprocessing datasets using AWS tools
• Data transformation and feature engineering in SageMaker
• Hands-on lab: Preparing a dataset for machine learning

Session 3: Building Your First Machine Learning Model

• Training a simple regression model using SageMaker’s built-in algorithms
• Evaluating model performance with basic metrics
• Hands-on lab: Building and testing a regression model

Day 2
Session 1: Training and Tuning Machine Learning Models

• Advanced training options in SageMaker: Distributed training and hyperparameter tuning
• Best practices for optimizing model performance
• Hands-on lab: Tuning a machine learning model in SageMaker

Session 2: Working with SageMaker Notebooks

• Using Jupyter Notebooks for custom model development
• Writing and running training scripts in SageMaker
• Hands-on lab: Implementing a custom ML workflow in a notebook

Session 3: Introduction to SageMaker Pipelines

• Automating ML workflows with SageMaker Pipelines
• Building reusable pipelines for end-to-end machine learning tasks
• Hands-on lab: Creating and running a SageMaker Pipeline

Day 3
Session 1: Deploying Machine Learning Models in AWS SageMaker

• Deploying trained models as real-time endpoints
• Batch inference and integrating predictions into applications
• Hands-on lab: Deploying and testing an ML model

Session 2: Monitoring and Managing Deployed Models

• Monitoring model performance with SageMaker Model Monitor
• Managing model versions and updates
• Hands-on lab: Monitoring a deployed model in AWS

Session 3: Case Study: Real-World ML Solution with SageMaker

• Solving a real-world business problem using SageMaker tools
• Group activity: Developing and presenting an end-to-end solution