Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, students will spend a day in the life of a data scientist so that students can collaborate efficiently with data scientists and build applications that integrate with ML. Students will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. Students will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.
Course Objectives
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
This course is intended for:
Development Operations (DevOps) engineers
Application developers
Prerequisites
AWS Technical Essentials
Entry-level knowledge of Python programming
Entry-level knowledge of statistics
Course Outline
- Course Welcome
- Module 1:Introduction to Machine Learning
- Module 2: Preparing a Dataset
- Module 3: Training a Model
- Module 4: Evaluating and Tuning a Model
- Module 5: Deploying a Model
- Module 6: Operational Challenges
- Module 7: Other Model-Building Tools
- Course Summary and Resources
