Operationalizing AI in game development: MLOps infrastructure patterns and frontline insights

Modern game development increasingly depends on sophisticated machine-learning (ML) workflows to drive personalization, procedural content, and adaptive AI behaviors at scale. Conventional MLOps playbooks, however, seldom satisfy the stringent latency, telemetry, and governance demands of live-service gaming. This article proposes a comprehensive end-to-end MLOps framework for game development, covering high-frequency telemetry and data-governance pipelines, rollback-capable player-centric feature stores, and a canonical GameOps–MLOps reference architecture that unifies asset and model delivery. Continuous-integration paradigms are extended with game-specific tests behavioral bots, balance regressions, and canary deployments in matchmaking queues while scalable training pipelines incorporate distributed GPU orchestration, curriculum-driven self-play, and privacy-preserving federated updates. The Real-Time Inference Mesh (RTIM) achieves sub-20 ms gRPC inference through edge caching, model hot-swap, and ensemble fallback, and online-learning loops embed reinforcement learning directly into live operations. AIOps layers couple gameplay KPIs with model health, enabling automated root-cause analysis and self-healing. The framework also details model-integrity attestation, cheat-detection pipelines, regulatory mapping, and cost-plus-carbon optimization. Case studies from indie to AAA contexts validate the approach, and a forward-looking research agenda concludes with an actionable roadmap for companies aiming to mature their game-centric MLOps capabilities.