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Nick Elprin, founder of Domino Data Lab, talks about how to deploy predictive models into production, specifically in the context of a corporate enterprise use case. Nick demonstrates an easy way to “operationalize” your predictive models by exposing them as low-latency web services that can be consumed by production applications. In the context of a real-world use case this translates into more subtle requirements for hosting predictive models, including zero-downtime upgrades and retraining/redeploying against new data. Nick also focuses on the best practices for writing code that will make your predictive models easier to deploy.

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Cliff Click Cliff Click on

In this talk on Machine Learning Distributed GBM, Earl Hathaway, resident Data Scientist at 0xdata, talks about distributed GBM, one of the most popular machine learning algorithms used in data mining competitions. He will discuss where distributed GBM is applicable, and review recent KDD & Kaggle uses of machine learning and distributed GBM. Also, Cliff Click, CTO of 0xdata, will talk about implementation and design choices of a Distributed GBM. This talk was recorded at the SF Data Mining meetup at Trulia.

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Laurent Gautier Laurent Gautier on

We were lucky to attend the Bay Area R users group last week where we recorded Laurent Gautier's talk on the RPy2 bridge which allows one to use Python as the glue language to develop applications while using R for the statistics and data analysis engine. He also demonstrated how a web application could be developed around an existing R script.

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