Responding to Prediction Requests in Milliseconds — As an applied data scientist at Zynga, I’ve started getting hands on with building and deploying data products. As I’ve explored more and more use cases for machine learning, there’s been an increasing need for real-time machine learning (ML) systems, where the system performs feature engineering and model inference to…

Tools for building real-time ML applications — Building and deploying data products that use machine learning to personalize products typically involves a variety of disciplines including data science, product management, and engineering. I’ve worked in a few organizations where data science and engineering teams are somewhat siloed, and the following approaches are used to put models into…

Making the move from Docker to Live Deployments — While 2020 has been a challenging year, I was able to use the transition to remote work to explore new tools to expand my data science skill set. It was the year that I made the transition from data scientist to applied scientist, where I was responsible for not only…

Building Data Products with Managed Services — In large companies, there are typically separate teams for training machine learning models and putting these models into production. A data science team may be responsible for feature engineering, model selection, and hyperparameter tuning, while a machine learning engineering team is responsible for building and maintaining the infrastructure required to…

Zynga Analytics at Spark Summit 2020 — Over the past two years, analytics at Zynga has been increasingly using PySpark, which is the Python interface to the Spark big data platform. We have central and embedded analytics teams that use PySpark to support mobile publishing operations including analytics and reporting, experimentation, personalization services, and marketing optimization. I…