MLOps Engineering on AWS Course

Course Code: GK7395

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Duration: 3 days

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Course Overview

MLOps Engineering on AWS applies DevOps principles to the machine learning lifecycle. It focuses on the processes, tools and collaboration required to build, train, deploy and monitor machine learning models reliably in production.

The course follows an MLOps maturity approach, progressing from experimentation through repeatable and reliable workflows. Delegates explore versioning, orchestration, CI/CD, governance, testing, deployment, scaling and monitoring, including techniques for detecting and responding to model and data drift.

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Target Audience

• MLOps engineers responsible for productionising and monitoring machine learning models
• DevOps engineers responsible for deploying and maintaining ML workloads
• Technical professionals working across data science, engineering and operations teams

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Course Objectives

• Explain the benefits of MLOps and how it differs from traditional DevOps.
• Assess security and governance requirements for ML use cases.
• Set up experimentation environments using Amazon SageMaker.
• Apply versioning practices to data, models and code.
• Create repeatable ML orchestration workflows.
• Implement CI/CD, automated packaging, testing and deployment for ML.
• Monitor models and detect performance degradation or data drift.
• Automate retraining and operational responses.

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Course Content

Day 1

• Introduction to MLOps
• People, process, technology, security and governance
• MLOps maturity models
• SageMaker Studio experimentation environments
• Data, model and code repositories
• ML pipeline orchestration

Day 2

• End-to-end orchestration with AWS Step Functions
• SageMaker Projects
• Third-party tools and human-in-the-loop workflows
• Governance and SageMaker security
• Scaling and multi-account strategies
• Testing and traffic shifting

Day 3

• Model deployment testing
• Traffic shifting
• Monitoring ML solutions
• Detecting data drift
• Remediating model performance issues
• Building and troubleshooting an ML pipeline

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Course Prerequisites

• AWS Technical Essentials or equivalent experience
• DevOps Engineering on AWS or equivalent practical experience
• Practical data science and Amazon SageMaker experience