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Tuesday, July 12 • 13:40 - 14:20
Challenges of Machine Learning at Scale

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Motivated by the problem of predicting whether any given ad would be clicked in response to a query, in this introductory talk we outline the requirements and large-system design challenges that arise when designing a machine learning system that makes millions of predictions per second with low latency, learns quickly from the responses to those predictions, and maintains a consistent level of model quality over time. We present alternatives for meeting those challenges using diagrams of machine learning pipelines.

Concepts used in this talk: machine learning (classification), software pipelines, sharding and replication, map-reduce

Speakers
avatar for Graham Poulter

Graham Poulter

SRE, Google
I work at Google Dublin as an SRE on machine learning pipelines used in Ads & Commerce, helping make them reliable and efficient, including not wasting human time on things like updating config and software. Originally from South Africa, I also facilitate technical training and enjoy... Read More →


Tuesday July 12, 2016 13:40 - 14:20
Lansdowne

Attendees (19)