HOW TO GET DATA SCIENCE MODELS INTO PRODUCTION ON A BUDGET
One of the biggest challenges we have as data scientists is getting our models into production. I’ve worked with Java developers to get models into production and there aren’t always the same libraries in Java as there are in Python. Example try porting Scikitlearn code to Java. Possible solution: PMML or you write spec.
An even better solution: I will explain how to use Science Ops from YhatHQ to build better data products. Specifically I will talk about how to use a Python, Pandas etc to build a model. Test it locally and then deploy it so thatdevelopers can get an easy to use RESTful API. I will remark some of my experiences from working with it, and give a use case and some architectural remarks. I’ll also give a run down of alternatives to Science Ops that I’ve found.
Pre Requisites - some experience with Pandas and the scientific Python would be beneficial. This talk is aimed at Data Science enthusiasts or professionals.