Rajat Arya - Deploying scikit learn Models in Production

PyData Dallas 2015 Machine Learning should be everywhere. Applications today have the opportunity to leverage all the data being collected about users' interactions and behavior. Unfortunately machine learning at scale is mostly absent from production systems. Training models using scikit-learn is useful, but it is difficult to take this code to production. Why is it so painful to deploy models in a scalable way? What are the options and what challenges exist today? After exploring the current options, I will present Dato Predictive Services, which we developed to address these challenges. Dato Predictive Services enables deploying and managing scikit-learn models into an elastic, scalable, fault-tolerant, low-latency cluster of machines, in AWS & YARN. With Dato Predictive Services, in one command, you can take arbitrary Python and deploy it as a REST service. This will be a hands-on talk, walking through code and with multiple demonstrations. Bring your laptop to follow along! View slides for this presentation here: http://www.slideshare.net/dato-inc/py-data-scikitproduction 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps