This course covers part of the deployment phase of the machine learning (ML) lifecycle. In this course, you will learn how to move a trained model into production and manage the infrastructure that serves it. This course covers selecting deployment targets and inference strategies on Amazon SageMaker AI, including real-time, serverless, asynchronous, and batch inference, and bringing in models built outside Amazon Web Services (AWS). You will also learn to deploy generative AI workloads, including foundation model hosting and agents with Amazon Bedrock and retrieval augmented generation (RAG) systems with Amazon Bedrock Knowledge Bases.
ML Engineer - Manage Deployment Infrastructure
Build in-demand skills with a full year of Coursera Plus, now $239. Save now.
ML Engineer - Manage Deployment Infrastructure
This course is part of AWS ML Engineer Associate Professional Certificate
Instructor: AWS Instructor
Included with Learn more
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Differentiate appropriate deployment infrastructure for ML and AI workloads based on latency, cost, and scale requirements.
Differentiate appropriate compute environments and deployment targets for ML workloads.
Differentiate deployment orchestrators and multi-model or multi-container strategies.
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
November 2026
Assessments
12 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your Machine Learning expertise
This course is part of the AWS ML Engineer Associate Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Amazon Web Services
There are 5 modules in this course
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor
Offered by
Explore more from Machine Learning
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
