AWS Certified Machine Learning Engineer – Associate (Machine Learning Engineering on AWS)

Course Overview

This three-day, instructor-led course helps you turn your machine learning expertise into production-ready solutions on AWS. You’ll learn how to build, deploy, and operationalize ML applications using Amazon SageMaker and EMR. Designed for ML professionals aiming for AWS certification, this hands-on course equips you with practical skills to implement enterprise-grade machine learning solutions. At the completion of this course, participants will be able to:

• Explain the fundamentals of machine learning and its applications on the AWS platform.
• Process, transform, and engineer data for machine learning tasks using AWS services, like Amazon SageMaker Data Wrangler, Amazon EMR, and SageMaker Processing.
• Select appropriate machine learning algorithms and modeling approaches based on problem requirements and model interpretability.
• Design and implement scalable machine learning pipelines using AWS services for model training, deployment, and orchestration.
• Create automated CI/CD pipelines for machine learning workflows.
• Discuss appropriate security measures for machine learning resources on AWS.
• Implement monitoring strategies for deployed machine learning models, including techniques for detecting data drift.

Schedule

Module 1: Introduction to Machine Learning (ML) on AWS

Module 2: Analyzing Machine Learning (ML) Challenges

Module 3: Data Processing for Machine Learning (ML)

Module 4: Data Transformation and Feature Engineering

Module 5: Choosing a Modeling Approach

Module 6: Training Machine Learning (ML) Models

Module 7: Evaluating and Tuning Machine Learning (ML) models

Module 8: Model Deployment Strategies

Module 9: Securing AWS Machine Learning (ML) Resources

Module 10: Machine Learning Operations (MLOps) and Automated Deployment

Module 11: Monitoring Model Performance and Data Quality

Module 12: Course Wrap-up