Curso: Machine Learning Engineering on AWS

Objectives

• Explain ML fundamentals and its applications in the AWS Cloud.
• Process, transform, and engineer data for ML tasks by using AWS services.
• Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.
• Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.
• Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
• Discuss appropriate security measures for ML resources on AWS.
• Implement monitoring strategies for deployed ML models, including techniques for detecting data drift

Course Outline

Module 0: Course Introduction

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