Machine Learning Engineering on AWS Course

Course Code: GK910029

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Duration: 3 days

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Course Overview

Machine Learning Engineering on AWS is an intermediate course for professionals who want to build, deploy and operationalise machine learning solutions at scale.

Delegates develop practical experience using AWS services including Amazon SageMaker AI and Amazon EMR. The course covers data processing and feature engineering, modelling approaches, training and deployment pipelines, orchestration, CI/CD, security, responsible machine learning and model monitoring.

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Target Audience

• Machine learning engineers
• Professionals moving into machine learning engineering roles
• DevOps engineers
• Developers and SysOps engineers supporting ML workloads

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Course Objectives

• Explain machine learning fundamentals and AWS ML services.
• Process, transform and engineer data for machine learning tasks.
• Select appropriate algorithms and modelling approaches.
• Build scalable model training and deployment pipelines.
• Use AWS services to orchestrate machine learning workflows.
• Create automated CI/CD pipelines for ML solutions.
• Apply appropriate security controls to ML resources.
• Monitor deployed models and identify issues such as data drift.

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Course Content

Day 1

• Introduction to machine learning on AWS
• Amazon SageMaker AI and responsible ML
• Analysing ML business challenges
• Training approaches and algorithms
• Data preparation and exploratory analysis
• Amazon EMR and AWS storage options
• Data transformation and feature engineering

Day 2

• Choosing modelling approaches
• Model training and tuning
• Evaluating machine learning models
• Building reusable ML workflows
• Deployment and inference patterns
• Scaling machine learning solutions

Day 3

• ML pipeline orchestration
• CI/CD for machine learning
• Security for ML workloads
• Production monitoring
• Detecting drift and operational issues
• Maintaining production-ready machine learning systems

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Course Prerequisites

Delegates should have a working understanding of machine learning concepts and basic AWS familiarity. Experience with Python and common data-processing workflows is beneficial.