Advanced Engineering for MLOps on AWS
The course “DSAI307 – Advanced Engineering for MLOps on AWS” provides a comprehensive guide on implementing MLOps (Machine Learning Operations) using Amazon Web Services (AWS). Students will gain practical skills through a series of modules covering an introduction to MLOps, creating experimental environments with Amazon SageMaker, evaluating security and governance requirements for ML models, and best practices for versioning and maintenance models, implementing CI/CD pipelines, and monitoring ML-based solutions.
CODE: DSAI307
Category: Artificial Intelligence Course
Teaching methodology
The course includes educational laboratories in which each student will be able to carry out training exercises that will provide practical experience in the use of the tool, for each of the topics covered during the course.Prerequisites
- Basic knowledge of AWS.
- DevOps Engineering AWS.
- Completing DSAI107 course.
Below is an overview of the course contents:
- Introduction to MLOps.
- Experimental environments in SageMaker Studio.
- Repositories.
- Orchestration.
- Scaling & Testing.
- Monitoring.
At the end of the course, participants will be able to:
- Explain the benefits of MLOps methodology.
- Compare and understand the differences between DevOps and MLOps.
- Create experimental environments for MLOps with Amazon SageMaker.
- Evaluate the security and governance requirements for an ML model and describe solutions and migration strategies.
- Explain best practices for versioning and maintaining model integrity.
- Describe three options for creating a CI/CD pipeline.
- Implement best practices to automate model deployment.
- Monitor ML-based solutions.
Duration – 3 days
Delivery – in the classroom, on site, remotely
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
- Instructor: English
- Laboratories: English
- Slides: English
