MLOps Engineering on AWS Training in Global

The MLOps Engineering on AWS Training course helps organisations automate the development, training, deployment, and operational management of machine learning models using AWS services and modern MLOps practices. By combining DevOps principles with Machine Learning Operations (MLOps), the course promotes effective collaboration between data engineers, data scientists, developers, and operations teams.

Participants will learn how to design and automate end-to-end machine learning workflows using Amazon SageMaker, AWS CodeBuild, Apache Airflow, Kubernetes, and other MLOps technologies. Key topics include model development, Continuous Training, CI/CD for Machine Learning, model deployment, performance monitoring, Data Drift and Model Drift detection, governance, and production-grade ML operations.

Through hands-on labs and real-world scenarios, learners will gain practical experience building scalable, secure, and reliable machine learning pipelines while applying industry best practices for managing ML workloads in production environments.

Required

• AWS Technical Essentials
• DevOps Engineering on AWS or equivalent experience
• Practical Data Science with Amazon SageMaker or equivalent experience

Recommended

• The Elements of Data Science
• Machine Learning Terminology and Process
• Familiarity with Python and machine learning concepts

By the end of this course, participants will be able to:

• Explain the principles of Machine Learning Operations (MLOps).
• Understand the key differences between DevOps and MLOps.
• Design end-to-end machine learning workflows.
• Build automated ML pipelines using Amazon SageMaker.
• Automate model training, testing, deployment, and retraining.
• Implement model packaging and deployment strategies.
• Monitor machine learning models in production.
• Detect Data Drift and Model Drift.
• Monitor and mitigate Model Bias.
• Implement Human-in-the-Loop AI review processes.

Day 1

Module 0: Welcome

• Course Introduction
• AWS AI & ML Ecosystem Overview
• MLOps Fundamentals

Module 1: Introduction to MLOps

Machine Learning Operations Fundamentals

• What is MLOps?
• MLOps Goals
• Data, Code, and Model Management
• Enterprise AI Operations

From DevOps to MLOps

• DevOps vs MLOps
• Machine Learning Lifecycle
• MLOps Workflows
• Cross-Team Collaboration

MLOps Use Cases

• Production AI Systems
• Enterprise Machine Learning Projects
• Operational Challenges and Solutions

Module 2: MLOps Development

Building Machine Learning Models

• Model Development
• Model Training
• Model Evaluation
• Security and Governance

Automation and Workflow Management

• Apache Airflow
• Kubernetes Integration
• Workflow Automation
• CI/CD for Machine Learning

Amazon SageMaker for MLOps

• Amazon SageMaker
• SageMaker Pipelines
• Automated ML Lifecycle Management

Hands-On Labs

• Bring Your Own Algorithm (BYOA)
• Building MLOps Pipelines
• Serving Models with AWS CodeBuild

MLOps Action Plan Workshop

• Organisational MLOps Strategy
• Transformation Planning

Day 2

Module 3: MLOps Deployment

Model Deployment

• Deployment Operations
• Model Packaging
• Version Control

Inference Strategies

• Batch Inference
• Real-Time Inference
• Asynchronous Inference

SageMaker Production Variants

• Canary Deployments
• Blue/Green Deployments
• Shadow Deployments

Edge AI Deployment

• Deploying ML Models to Edge Devices
• Edge AI Use Cases

Hands-On Labs

• Production Deployment
• A/B Testing
• Deployment Validation

Day 3

Module 4: Model Monitoring and Operations

Monitoring Machine Learning Models

• Performance Monitoring
• Observability
• Operational Metrics

Data Drift and Model Drift

• Detecting Data Changes
• Model Performance Degradation
• Drift Management Strategies

Monitoring Model Bias

• Fair AI Principles
• Bias Detection and Mitigation

Amazon SageMaker Monitoring Tools

• Amazon SageMaker Model Monitor
• Model Registry
• Feature Store
• Pipeline Observability

Human-in-the-Loop AI

• Human Review Processes
• Model Validation Workflows

Hands-On Labs

• Pipeline Troubleshooting
• Model Monitoring
• Drift Detection
• Bias Analysis

Module 5: Wrap-Up

• Course Review
• Enterprise MLOps Strategy
• Best Practices
• Future of AI Operations