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. …

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…

Building User Profiles with Streaming Data — For the majority of my data science career, I’ve built machine learning models using data fetched from a data warehouse or lake. With this approach, you can create a feature vector for each user by applying SQL commands to transform several events into a user summary. …

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…

While Python and R provide rich ecosystems for data scientists to handle a wide range of problems, there are situations in which other programming languages, including Java and Go, should be explored. I have found that hands-on experience with Java has been increasingly useful as I shift my focus from batch…

HTTPS and Access Control — If you’re looking to host a machine learning service over the web, then it’s usually necessary to lock down the endpoint so that calls to the service are secure and only authorized users are able to access the service. In order to make sure that sensitive information is not exposed…

Deploying Production-Grade Containers for Model Serving — One of the functions of data science teams is building machine learning (ML) models that provide predictive signals for products and personalization. While DevOps has not always been considered a core responsibility of data science teams, it is becoming increasingly important as these teams start to take more ownership of…