I’m happy to be a part of the PyData speaking community by speaking at my first PyCon.
Here is the abstract and then some remarks 🙂
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.
Firstly you can check out www.pydata.it for the PyData focused schedule
Secondly: My slides are here https://speakerdeck.com/springcoil/data-products-or-getting-models-into-production which you can look at before the talk if you wish.
Finally: I provide here a link to the code I’ll mention in the talk – this is a simple example of how you would build an ODE using the PyData stack. The code isn’t excellent, but it is functional and easy to read.