Speaker
Description
MLOps - Going from Good to Great
To build a highly-performant machine learning model is not a small feat. The process requires a well-curated dataset, a suitable algorithm as well as finely tuned hyperparameters of the very algorithm. Once an ML model reaches a certain degree of maturity and is shared with a broader user base, a new set of operational challenges come to play. The growing field of MLOps addresses these challenges to ease the friction related to model distribution. In this lecture and exercise session, we will explore and practice main MLOps aspects, including but not limited to:
1. Selection and versioning of training datasets
2. Reproducibility of models and computing environments
3. Model encapsulation with HTTP API
4. Model versioning and roll-out strategies
5. Monitoring of model performance and its drift over time
