Machine Learning Engineering on AWS [GK910029]

OVERVIEW

Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.

Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.

Course level: Intermediate

OBJECTIVES

In this course, you will learn to do the following:

• 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.

AUDIENCE

This course is designed for professionals who are interested in building, deploying, and operationalizing machine learning models on AWS. This could include current and in-training machine learning engineers who might have little prior experience with AWS. Other roles that can benefit from this training are DevOps engineer, developer, and SysOps engineer.

CONTENT

Day 1

Module 0: Course Introduction

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

Topic 1A: Introduction to ML
Topic 1B: Amazon SageMaker AI
Topic 1C: Responsible ML

Module 2: Analyzing Machine Learning (ML) Challenges

Topic 2A: Evaluating ML business challenges
Topic 2B: ML training approaches
Topic 2C: ML training algorithms

Module 3: Data Processing for Machine Learning (ML)

Topic 3A: Data preparation and types
Topic 3B: Exploratory data analysis
Topic 3C: AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering

Topic 4A: Handling incorrect, duplicated, and missing data
Topic 4B: Feature engineering concepts
Topic 4C: Feature selection techniques
Topic 4D: 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

Topic 5A: Amazon SageMaker AI built-in algorithms
Topic 5B: Selecting built-in training algorithms
Topic 5C: Amazon SageMaker Autopilot
Topic 5D: Model selection considerations
Topic 5E: ML cost considerations

Module 6: Training Machine Learning (ML) Models

Topic 6A: Model training concepts
Topic 6B: Training models in Amazon SageMaker AI
Lab 3: Training a model with Amazon SageMaker AI

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

Topic 7A: Evaluating model performance
Topic 7B: Techniques to reduce training time
Topic 7C: Hyperparameter tuning techniques
Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Module 8: Model Deployment Strategies

Topic 8A: Deployment considerations and target options
Topic 8B: Deployment strategies
Topic 8C: Choosing a model inference strategy
Topic 8D: Container and instance types for inference
Lab 5: Shifting Traffic A/B

Day 3

Module 9: Securing AWS Machine Learning (ML) Resources

Topic 9A: Access control
Topic 9B: Network access controls for ML resources
Topic 9C: Security considerations for CI/CD pipelines

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

Topic 10A: Introduction to MLOps
Topic 10B: Automating testing in CI/CD pipelines
Topic 10C: 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

Topic 11A: Detecting drift in ML models
Topic 11B: SageMaker Model Monitor
Topic 11C: Monitoring for data quality and model quality
Topic 11D: Automated remediation and troubleshooting
Lab 7: Monitoring a Model for Data Drift

Module 12: Course Wrap-up