Many companies today deal with the need to deploy their hard earned machine learning knowledge for as many clients as possible. This challenge is impossible without robust automation of the machine learning systems.
However, machine learning automation is different than most engineering pursuits, since a critical part of the development takes place after deploying the system, where we learn if the ideas hatched in the lab actually scale and fit enough real world scenarios.
We’ll consider the different components needed to automate such a system, and various pitfalls encountered during this development, illustrated by war stories.
The talk will go over:
Senior data scientist and hacker at LivePerson, and tech lead for the research team. After getting a math Ph.D., I’ve spent the last seven years developing classification and recommendation systems over big data, taking them from the drawing board to production.
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