Data science teams tend to put a lot of thought into developing a predictive model to address a business need, tackling data processing, model development, training and validation. After validation, the model then tends to get rolled out -- without much testing -- into production. While software engineering best practices have been around for a long time, until recently, no formal guidelines existed for checking the quality of code of a machine learning pipeline.
The talk will cover tips and best practices for writing more robust production-ready predictive model pipelines. We know that code is never perfect; Irina will also share the pains and lessons learned from experience productionalizing and maintaining 4 customer-facing models at 4 different companies: in online advertising, consulting, finance, and fashion.