About
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.
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The talk
Credits and licence
- Institution
- CERN
- Department
- Inverted CSC
- Speaker
- Michal Maciejewski
- Year
- 2023
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed