Training & Monitoring AI - Drift Detection • Thomas Viehmann • GOTO 2022
This presentation was recorded at GOTO Copenhagen 2022. #GOTOcon #GOTOcph
Thomas Viehmann - Co-Author of “Deep Learning with PyTorch“ @PyTorch
RESOURCES
ABSTRACT
Do our deployed AIs work as expected?
Before we deploy AI models, we extensively validate them to make sure they perform well. But how confident are we that we get comparable performance after deployment? How can we check whether the assurances we took from our validation are still applicable?
To answer these questions and sleep well again, drift detection is a key aspect of monitoring deployed models. It checks whether the information flowing through our models - either probed at the input, output or somewhere in-between - is still consistent with the one it was trained and evaluated on.
Thomas shows things that can go wrong when we neglect drift detection, looks at how drift detection works, and then demonstrates how effective drift detection can be implemented in practice. He draws on his experience with implementing the open-source , the turn-key solution , as well as consulting with clients to implement drift detection in their processes. [...]
TIMECODES
00:00 Intro
05:12 Agenda
05:53 Why detect drift?
12:51 Statistics of detecting drift
21:21 Practical drift detection
32:02 Conclusion
33:52 Outro
Download slides and read the full abstract here:
RECOMMENDED BOOKS
Thomas Viehmann, Eli Stevens & Luca Antiga • Deep Learning with PyTorch •
Stefan Helzle • Low-Code Application Development with Appian •
Phil Winder • Reinforcement Learning •
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) •
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns •
#PyTorch #DriftDetection #MonitoringAI #AI #ML #DataScience #ArtificialIntelligence #DeepLearning #NeuralNetworks #MathInf #TorchDrift #DriftDash #ThomasViehmann #Programming #SoftwareEngineering #SoftwareDevelopment
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