However, we use Machine Learning (ML) in many other ways across our company to make our games, systems and products better. The focus of this post is primarily “classic” supervised ML, but we’re also actively prototyping generative models (e.g. LLMs) in a number of key areas. Before diving into the details of our ML and AI initiatives, it’s important to note that our ability to train and deploy large-scale ML models is due to our investment in a strong data infrastructure. We have dedicated significant time to building the proper logging & telemetry, identifying reliable metrics, and curating labels so that our models are both accurate and useful.