Mom, I messed up the production deployment of my AI algorithm!
About the talk
It's a well-known statistic in the field: the vast majority of AI projects fail. But the most discouraging failures, with the strongest business impact, are those that occur once the model is in production: a bad recommendation model breaks user trust in the algorithm, a bad facial recognition model prevents unlocking one's phone, a bad pedestrian detection model can cause a fatal accident...
Over the past 4 years, I have deployed several AI algorithms in production across different contexts: each presented difficulties and learnings that contributed to shaping my convictions on how to properly deploy AI algorithms (MLOps). I share the most interesting ones with you in this talk:
1) On the importance of load testing
2) Watch out, my data is drifting!
3) How to know if my model works in real life?
4) Let's (re)discover together through these experiences the fundamentals of ML monitoring!
Speaker(s)
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