AMA30 - Practical Data Science with Amazon SageMaker
Course description
Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.
Activities
This course includes presentations, hands-on labs, and demonstrations.
Course objectives
In this course, you will learn to: Discuss the benefits of different types of machine learning for solving business problems Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems Explain how data scientists use AWS tools and ML to solve a common business problem Summarize the steps a data scientist takes to prepare data Summarize the steps a data scientist takes to train ML models Summarize the steps a data scientist takes to evaluate and tune ML models Summarize the steps to deploy a model to an endpoint and generate predictions Describe the challenges for operationalizing ML models Match AWS tools with their ML function
Intended audience
This course is intended for: Development Operations (DevOps) engineers Application developers
Prerequisites
We recommend that attendees of this course have: AWS Technical Essentials Entry-level knowledge of Python programming Entry-level knowledge of statistics
Course outline
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 optimization Hands-On Lab: Model Tuning and Hyperparameter Optimization 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