What you'll learn
- How to deploy your own models in the AWS Cloud
- How to automate workflows for building, training, testing, and deploying ML models
- The different deployment strategies for implementing ML models in production
- How to monitor for data drift and concept drift that could affect prediction and alignment with business expectations
- And much more
Who should take this course
- ML data platform engineers
- DevOps engineers
- Developers/operations staff with responsibility for operationalizing ML models
What experience you'll need
- 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
Type: Classroom (virtual and in person)
Length: 3 days
This course is offered in the following languages: Bahasa Indonesia, English, French (France), German, Italian, Japanese, Korean, Portuguese (Brazil), Simplified Chinese, Spanish (Latin America), Spanish (Spain), and Traditional Chinese.
We regularly update our courses based on customer feedback and AWS service updates. As a result, course content may vary between languages while we localize these updates.
Need more information?
Download the course outline for more information about what this course covers.
Looking for private training for your team?
With AWS-delivered private training, your team will learn actionable best practices together, tailored to your specific use cases.
Thinking about taking an exam?
Find a related exam to reinforce your learning.