Implementing Feature and Metric Stores for Machine Learning Models in the Gaming Industry

Abstract

Machine learning models are used in content creation and generate real-time observations in gaming with a positive effect on both performance and production processes. However, the management and deployment of these features and metrics for the purposes of these benefits are critical. Looking at feature and metric stores data structures that are used for storing and retrieving feature and metric data for machine learning models. Feature stores are responsible for featuring storage and delivery for model training and features needed for model’s inferencing, whereas metric stores contain metrics required for the assessment of specific models. The adoption of these stores can drastically bring down the amount of development time and effort as well as enhance the aptitude of recognizing real time actions and quality of the game. It is therefore prudent and helpful for the reader to learn the basic concepts that underpin feature and metric store, the ways in which gaming application benefits from them, and the possible ways in which this technology can be developed further in the future.