Practical Data Science with Amazon SageMaker

Course subjects

Module 1: Introduction to Machine Learning

• Benefits of machine learning (ML)

• Types of ML approaches

• Framing the business problem

• Prediction quality

• Processes, roles, and responsibilities for ML projects

Module 2: Preparing a Dataset

• Data analysis and preparation

• Data preparation tools

• Demonstration: Review Amazon SageMaker Studio and Notebooks

• Hands-On Lab: Data Preparation with SageMaker Data Wrangler

Module 3: Training a Model

• Steps to train a model

• Choose an algorithm

• Train the model in Amazon SageMaker

• Hands-On Lab: Training a Model with Amazon SageMaker

• Amazon CodeWhisperer

• Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks

Module 4: Evaluating and Tuning a Model

• Model evaluation

• Model tuning and hyperparameter optimisation

• Hands-On Lab: Model Tuning and Hyperparameter Optimisation with Amazon SageMaker

Module 5: Deploying a Model

• Model deployment

• Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction

Module 6: Operational Challenges

• Responsible ML

• ML team and MLOps

• Automation

• Monitoring

• Updating models (model testing and deployment)

Module 7: Other Model-Building Tools

• Different tools for different skills and business needs

• No-code ML with Amazon SageMaker Canvas

• Demonstration: Overview of Amazon SageMaker Canvas

• Amazon SageMaker Studio Lab

• Demonstration: Overview of SageMaker Studio Lab

• (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint

Please note: This is an emerging technology course. Course outline is subject to change as needed.