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FRCAWS-22AWS

MLOps Engineering on AWS

Duration · 3 daysVirtual + In-PersonInstructor-Led

$2,050

Course Description

This three-day instructor-led course teaches learners how to operationalize machine learning workloads on Amazon Web Services using MLOps principles, automation, CI/CD pipelines, monitoring, governance, and scalable deployment strategies. Participants gain hands-on experience building, deploying, automating, and monitoring machine learning workflows using AWS services and modern DevOps practices.

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Audience Profile

This course is intended for:

  • Machine Learning Engineers
  • Data Engineers
  • DevOps Engineers
  • Cloud Engineers
  • AI Platform Engineers
  • Solutions Architects
  • Data Scientists transitioning to production ML

Prerequisites

Before enrolling, you should have:

  • Basic understanding of machine learning concepts
  • Familiarity with Python programming
  • Experience with AWS core services
  • Understanding of software development lifecycle concepts
  • Basic knowledge of CI/CD pipelines and Git workflows

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1By the end of the course, you’ll understand how to deploy machine learning models, take action when the model prediction in production drifts from agreed-upon key performance indicators, and automate the full ML lifecycle—from code to successful ML deployment in the AWS cloud.
  • 2Implement DevOps best practices in machine learning workflows
  • 3Design, deploy, and monitor secure, scalable ML pipelines
  • 4Use Amazon SageMaker for experimentation, tuning, and deployment
  • 5Automate CI/CD workflows for ML models, data, and code
  • 6Take action when the model prediction drifts from KPIs
  • 7Extend the DevOps approach to ML teams including data scientists, data engineers, and software developers

— Day-by-Day —

Course Outline

Day 1 — Foundations of MLOps on AWS

Module 1

Introduction to MLOps

Topics

  • What is MLOps?
  • Differences between DevOps and MLOps
  • Machine learning lifecycle overview
  • Challenges in operationalizing ML
  • Benefits of automation and reproducibility
  • MLOps maturity models
  • AWS AI/ML ecosystem overview

Hands-On Lab

  • Explore the AWS ML services portfolio
  • Configure the development environment
  • Create IAM roles and permissions for ML workflows

Module 2

AWS Architecture for MLOps

Topics

  • Designing scalable ML platforms on AWS
  • Reference architectures for MLOps
  • Multi-account strategies
  • Security and governance considerations
  • Data storage options for ML
  • Networking considerations
  • Infrastructure as Code fundamentals
  • AWS Services Covered
  • Amazon SageMaker
  • Amazon S3
  • AWS IAM
  • AWS CloudFormation
  • Amazon ECR
  • AWS KMS
  • Amazon VPC

Hands-On Lab

  • Build foundational AWS infrastructure for MLOps
  • Configure secure ML networking architecture

Module 3

Data Engineering and Feature Pipelines

Topics

  • Data ingestion patterns
  • Data versioning concepts
  • Feature engineering workflows
  • Building reusable feature pipelines
  • Batch vs. streaming data processing
  • Data quality validation
  • Managing training datasets
  • AWS Services Covered
  • AWS Glue
  • Amazon Athena
  • Amazon EMR
  • Amazon Kinesis
  • AWS Lake Formation

Hands-On Lab

  • Build a data ingestion pipeline
  • Prepare and validate ML datasets

Module 4

Experimentation and Model Development

Topics

  • ML experimentation workflows
  • Tracking experiments and metrics
  • Hyperparameter optimization
  • Distributed training concepts
  • Containerized ML development
  • Reproducible model training
  • Notebook management best practices
  • AWS Services Covered
  • Amazon SageMaker Studio
  • SageMaker Training Jobs
  • SageMaker Experiments
  • SageMaker Automatic Model Tuning

Hands-On Lab

  • Train and tune a machine learning model
  • Track model experiments and metrics

Day 2 — Automating ML Workflows and CI/CD

Module 5

Building Automated ML Pipelines

Topics

  • CI/CD principles for machine learning
  • ML pipeline orchestration
  • Automating data preparation
  • Automated model training pipelines
  • Pipeline parameterization
  • Reusable pipeline components
  • Workflow orchestration strategies
  • AWS Services Covered
  • Amazon SageMaker Pipelines
  • AWS Step Functions
  • AWS Lambda

Hands-On Lab

  • Create an end-to-end SageMaker pipeline
  • Automate training and evaluation workflows

Module 6

Source Control and Continuous Integration

Topics

  • Git workflows for ML teams
  • Version control for datasets and models
  • CI pipelines for ML code
  • Automated testing strategies
  • Unit testing for ML applications
  • Container image management
  • AWS Services Covered
  • AWS CodeCommit
  • AWS CodeBuild
  • AWS CodePipeline
  • Amazon ECR

Hands-On Lab

  • Build a CI pipeline for ML training code
  • Automate testing and container builds

Module 7

Model Deployment Strategies

Topics

  • Real-time inference vs. batch inference
  • Deployment patterns for ML
  • Blue/green deployments
  • Canary deployments
  • Multi-model endpoints
  • Serverless inference
  • Edge deployment concepts
  • AWS Services Covered
  • SageMaker Endpoints
  • SageMaker Batch Transform
  • SageMaker Serverless Inference
  • AWS Lambda
  • Amazon ECS
  • Amazon EKS

Hands-On Lab

  • Deploy models using SageMaker endpoints
  • Implement canary deployment strategies

Module 8

Infrastructure Automation for ML

Topics

  • Infrastructure as Code for MLOps
  • Automating ML environments
  • Environment consistency
  • Scaling ML infrastructure
  • Cost optimization strategies
  • Policy-based governance
  • AWS Services Covered
  • AWS CloudFormation
  • AWS CDK
  • Terraform concepts on AWS

Hands-On Lab

  • Deploy repeatable ML infrastructure templates
  • Automate environment provisioning

Day 3 — Monitoring, Governance, and Production Operations

Module 9

Monitoring and Observability for ML Systems

Topics

  • Monitoring production ML systems
  • Model drift detection
  • Data drift monitoring
  • Performance monitoring
  • Logging and tracing
  • Operational dashboards
  • Alerting and incident response
  • AWS Services Covered
  • Amazon CloudWatch
  • SageMaker Model Monitor
  • AWS CloudTrail

Hands-On Lab

  • Configure model monitoring
  • Build dashboards and alerts for ML endpoints

Module 10

Model Governance and Responsible AI

Topics

  • Model governance frameworks
  • Bias detection and explainability
  • Compliance and auditing
  • Security best practices for ML
  • Managing model lineage
  • Approval workflows
  • Responsible AI principles
  • AWS Services Covered
  • SageMaker Clarify
  • SageMaker Model Registry
  • AWS IAM
  • AWS Config

Hands-On Lab

  • Register and approve ML models
  • Evaluate model bias and explainability

Module 11

Scaling and Operating ML in Production

Topics

  • High availability for ML systems
  • Multi-region deployment considerations
  • Performance optimization
  • GPU and accelerated computing
  • Cost management for ML workloads
  • Disaster recovery planning
  • Operational playbooks
  • AWS Services Covered
  • Amazon EC2
  • Elastic Load Balancing
  • Auto Scaling
  • Amazon EKS
  • AWS Backup

Hands-On Lab

  • Scale inference endpoints
  • Optimize performance and cost

Module 12

End-to-End MLOps Capstone Project

Topics

  • Designing a production-ready ML workflow
  • Integrating CI/CD with ML pipelines
  • Deploying and monitoring models
  • Governance and compliance implementation
  • Operational troubleshooting
  • Capstone Lab
  • Participants build a complete MLOps solution that includes:
  • Data ingestion
  • Automated training pipeline
  • Model registry integration
  • CI/CD automation
  • Model deployment
  • Monitoring and alerting
  • Governance controls
  • Course Wrap-Up
  • Final Review
  • Review MLOps best practices
  • Common implementation challenges
  • AWS Well-Architected considerations for ML
  • Operational excellence recommendations
  • Skills Validation
  • Knowledge checks
  • Practical assessments
  • Capstone presentation and review
  • Next Steps
  • AWS certification recommendations
  • Advanced MLOps learning paths
  • Production implementation guidance
  • AWS Services Covered
  • Amazon SageMaker
  • Amazon S3
  • AWS IAM
  • AWS Glue
  • Amazon Athena
  • Amazon EMR
  • Amazon Kinesis
  • AWS Lambda
  • AWS Step Functions
  • AWS CodePipeline
  • AWS CodeBuild
  • AWS CodeCommit
  • Amazon ECR
  • Amazon ECS
  • Amazon EKS
  • Amazon CloudWatch
  • AWS CloudFormation
  • AWS CDK
  • AWS Config
  • AWS CloudTrail
  • Amazon EC2

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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