Course overview
This course explores the concept of maintainability in AI systems, focusing on how to ensure long-term reliability, performance, and adaptability of machine learning models in production. Learners will gain an understanding of the entire data science pipeline, from data collection to model deployment, with an emphasis on what happens after deployment.
What you will learn:
- How to select and justify AI model deployment strategies.
- How to detect and address post-deployment performance issues.
- How to match system failures with causes and countermeasures.
- How to apply MLOps workflows for reliable AI operations.
- How to use monitoring tools to manage data drift and model decay.
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