Drifting Away: Testing ML Models in Production
Ғылым және технология
Deploying machine learning models has become a relatively frictionless process. However, properly deploying a model with a robust testing and monitoring framework is a vastly more complex task. There is no one-size-fits-all solution when it comes to productionizing ML models, oftentimes requiring custom implementations utilising multiple libraries and tools. There are however, a set of core statistical tests and metrics one should have in place to detect phenomena such as data and concept drift to prevent models from becoming unknowingly stale and detrimental to the business.
Combining our experiences from working with Databricks customers, we do a deep dive on how to test your ML models in production using open source tools such as MLflow, SciPy and statsmodels. You will come away from this talk armed with knowledge of the key tenets for testing both model and data validity in production, along with a generalizable demo which uses MLflow to assist with the reproducibility of this process.
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Пікірлер: 14
Hi, well organized demo. thank you. How can I access the notebooks?
@saivardhanpallerla9
Жыл бұрын
drive.google.com/drive/folders/1-uMqTjsU2I7ODLq5wUpAdBHFkNjvZDVD
Hi, a great session. Can we get access to the code?
Very interesting!
can I get the git repo for the notebook
@allthingsdata
Жыл бұрын
drive.google.com/drive/folders/1-uMqTjsU2I7ODLq5wUpAdBHFkNjvZDVD
@saivardhanpallerla9
Жыл бұрын
drive.google.com/drive/folders/1-uMqTjsU2I7ODLq5wUpAdBHFkNjvZDVD
This video will not more useful when notebook, repo access will be shared. Right now no links were shared sadly.
Might I know where to have slides? Its excellent talk!
@itsshaikaslam
2 жыл бұрын
notebooks and slides are here : drive.google.com/drive/folders/1-uMqTjsU2I7ODLq5wUpAdBHFkNjvZDVD
@allthingsdata
Жыл бұрын
drive.google.com/drive/folders/1-uMqTjsU2I7ODLq5wUpAdBHFkNjvZDVD
@saivardhanpallerla9
Жыл бұрын
drive.google.com/drive/folders/1-uMqTjsU2I7ODLq5wUpAdBHFkNjvZDVD