Elevate your skills with 'MLOps Engineering on AWS,' designed for developers and operations professionals aiming to seamlessly build, train, and deploy ML models using AWS's powerful suite of tools.
EnrollDescribe machine learning operations and workflows
Implement automation for ML workflows on AWS
Monitor and manage ML model performance in production
Integrate DevOps principles in MLOps for efficient model deployment
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. In this course, you will learn to:
August 12-14, 2025
9:00 AM - 5:00 PM
Virtual: Online - US/Eastern
$2025
Course introduction
Machine learning operations
Goals of MLOps
Communication
From DevOps to MLOps
ML workflow
Scope
MLOps view of ML workflow
MLOps cases
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
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
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
Course review
Activity: MLOps Action Plan Workbook
Wrap-up
Your team deserves training as unique as they are.
Let us tailor the course to your needs at no extra cost.
Trusted by Engineers at:
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier