Machine Learning Engineering on AWS Duration: 3 Days

Course details

Day 1

Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS
• Introduction to ML
• Amazon SageMaker AI
• Responsible ML

Module 2: Analyzing Machine Learning (ML) Challenges
• Evaluating ML business challenges
• ML training approaches
• ML training algorithms

Module 3: Data Processing for Machine Learning (ML)
• Data preparation and types
• Exploratory data analysis
• AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering
• Handling incorrect, duplicated, and missing data
• Feature engineering concepts
• Feature selection techniques
• AWS data transformation services
• Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon
EMR
• Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Day 2

Module 5: Choosing a Modeling Approach
• Amazon SageMaker AI built-in algorithms
• Amazon SageMaker Autopilot
• Selecting built-in training algorithms
• Model selection considerations
• ML cost considerations

Module 6: Training Machine Learning (ML) Models
• Model training concepts
• Training models in Amazon SageMaker AI
• Lab 3: Training a model with Amazon SageMaker AI

Module 7: Evaluating and Tuning Machine Learning (ML) models
• Evaluating model performance
• Techniques to reduce training time
• Hyperparameter tuning techniques
• Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Module 8: Model Deployment Strategies
• Deployment considerations and target options
• Deployment strategies
• Choosing a model inference strategy
• Container and instance types for inference
• Lab 5: Shifting Traffic

Day 3

Module 9: Securing AWS Machine Learning (ML) Resources
• Access control
• Network access controls for ML resources
• Security considerations for CI/CD pipelines

Module 10: Machine Learning Operations (MLOps) and Automated Deployment
• Introduction to MLOps
• Automating testing in CI/CD pipelines
• Continuous delivery services
• Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry
with Amazon SageMaker Studio

Module 11: Monitoring Model Performance and Data Quality
• Detecting drift in ML models
• SageMaker Model Monitor
• Monitoring for data quality and model quality
• Automated remediation and troubleshooting
• Monitoring a Model for Data Drift

Module 12: Course Wrap-up