MLOps Engineering on AWS

RM5,400.00 RM5,724.00 (after 6% SST)

Course duration: 3 Days


Course Objective:

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.
The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors.




  • AWS Technical Essentials course (classroom or digital) 
  • DevOps Engineering on AWS course, or equivalent experience 
  • Practical Data Science with Amazon SageMaker course, or equivalent experience


  • The Elements of Data Science (digital course), or equivalent experience 
  • Machine Learning Terminology and Process (digital course)



  • DevOps engineers 
  • ML engineers 
  • Developers/operations with responsibility for operationalizing ML models




Day 1 

Module 0: Welcome 
  • Course introduction 
Module 1: Introduction to MLOps
  • Machine learning operations 
  • Goals of MLOps 
  • Communication 
  • From DevOps to MLOps 
  • ML workflow 
  • Scope  MLOps view of ML workflow  MLOps cases
Module 2: MLOps Development 
  • Intro to build, train, and evaluate machine learning models 
  • MLOps security 
  • Automating 
  • Apache Airflow 
  • Kubernetes integration for MLOps 
  • Amazon SageMaker for MLOps 
  • Lab: Bring your own algorithm to an MLOps pipeline 
  • Demonstration: Amazon SageMaker 
  • Intro to build, train, and evaluate machine learning models 
  • Lab: Code and serve your ML model with AWS CodeBuild
  • Activity: MLOps Action Plan Workbook


Day 2 

Module 3: MLOps Deployment
  • Introduction to deployment operations 
  • Model packaging 
  • Inference 
  • Lab: Deploy your model to production 
  • SageMaker production variants 
  • Deployment strategies 
  • Deploying to the edge 
  • Lab: Conduct A/B testing
  • Activity: MLOps Action Plan Workbook 


Day 3

Module 4: Model Monitoring and Operations 
  • Lab: Troubleshoot your pipeline 
  • The importance of monitoring 
  • Monitoring by design 
  • Lab: Monitor your ML model 
  • Human-in-the-loop 
  • Amazon SageMaker Model Monitor 
  • Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store 
  • Solving the Problem(s) 
  • Activity: MLOps Action Plan Workbook 
Module 5: Wrap-up 
  • Course review 
  • Activity: MLOps Action Plan Workbook 
  • Wrap-up