Building low latency gradient boosted tree predictors on distributed platform- György Móra |Crunch19
Автор: CraftHub Events
Загружено: 2019-11-13
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At Ekata we evaluate identity information in online shopping transactions, to enable our customers (vendors) to protect against fraud. We make commitments to deliver pre-purchase verification predictions with very low latency.
When we built the ML system powering the Identity Check Confidence Score we faced the following challenges:
The pipeline that trains our model had to digest a large amount of transaction data
We had to perform a large set of validation experiments to ensure each new release caused no disruptions for our customers
At the same time, the model had to perform in a real time, low latency environment
In this talk I discuss how we met these challenges by building a Spark- and XGBoost-based training and experimentation pipeline that delivers our models as a custom predictor library, to allow seamless integration with the production environment. Our embeddable library enables super-fast predictions real-time. - Captured Live on Ustream at https://www.ustream.tv/channel/JUMjvC...
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