Tuesday Oct 3
14:50 –
15:40
Location: A2+A3

One does not simply put Machine Learning into Production

Slides:
Video:

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When deciding to infuse existing products with machine-learning smarts, or building ML-first products, there are multiple challenges to be aware of. First, you and your organization need to understand important dimensions -- accuracy, cost, maintainability, interpretability -- and trade-offs between them. Second, several technical challenges present themselves when deploying data science experiments into production environments. I will share some lessons learned while building ML products serving billions of predictions to live customers -- and hopefully provide some take-aways for anyone in the audience looking to indeed put machine learning into production.

ai
machine learning
machine learning in production
data science
Henrik Brink
Author of "Real-World Machine Learning"
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