IDEAS SoCal AI & Data Science Conference 2018

Reducing the gap between R&D and production is still a challenge for data science/ machine learning engineering groups in many companies. Typically, data scientists develop the data-driven models in a research-oriented programming environment (such as R and python). Next, the data/machine learning engineers rewrite the code (typically in another programming language) in a way that is easy to integrate with production services. This process has some disadvantages: 1) It is time consuming; 2) slows the impact of data science team on business; 3) code rewriting is prone to errors. A possible solution to overcome the aforementioned disadvantages would be to implement a deployment strategy that easily embeds/transforms the model created by data scientists. Packages and products such as jPMML, MLeap, PFA, Amazon SageMaker, and PMML among others are developed for this purpose. In this talk we review some of the mentioned packages along with a coding exercise, motivated by a real world project at Meredith Corp. The project involves development of a near real-time recommender system, which includes a predictor engine, paired with a set of business rules.