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 couse is offered in the following language: English.
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.
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