How Discord Made Distributed Compute Easy for ML Engineers (discord.com)

Discord described how it scaled its ML efforts from simple classifiers to production systems serving hundreds of millions of users by building a developer-friendly distributed compute platform. Faced with multi-GPU training needs and datasets too large for single machines, the team adopted Ray as the distributed-compute foundation and layered developer tooling, orchestration, and observability on top. The result: a workflow that made distributed ML “easy” for engineers — moving the company from ad‑hoc experiments to repeatable, production-grade pipelines and enabling models like Ads Ranking to produce a reported +200% improvement in business metrics. Technically, Discord’s stack couples Ray with orchestration via Dagster and KubeRay (Ray on Kubernetes), a custom CLI to simplify job submission, and an observability layer called X‑Ray to track performance and debugging across distributed jobs. This combination addresses core operational needs—multi‑GPU scheduling, data locality for large datasets, lifecycle orchestration, and end‑to‑end monitoring—while prioritizing developer experience. For the AI/ML community, Discord’s approach is a practical blueprint: leveraging Ray plus Kubernetes-friendly orchestration and strong observability lowers the barrier to scaling models, accelerates iteration, and turns distributed training from a specialist task into a standard engineering workflow.