Successfully deploying your diffusion model is the first step; maintaining its health and efficiency in production is an ongoing process. This chapter shifts focus to the operational management required once your model is live.

You will learn the essential practices for monitoring deployed diffusion models effectively. We will examine how to identify and track key performance metrics, including generation latency ($L_{gen}$), request throughput ($T_{req}$), error rates, and hardware utilization like GPU usage ($U_{gpu}$). We'll cover setting up comprehensive logging and tracing to diagnose issues, utilizing common monitoring tools and platforms, and establishing methods to detect performance regressions or shifts in output quality. Additionally, strategies for managing infrastructure costs and implementing safe model update procedures using CI/CD principles will be discussed. By the end of this chapter, you will understand how to keep your scaled diffusion model deployment running smoothly and reliably.