Deploying a large language model is a significant step, but the operational work continues long after the initial launch. Maintaining performance, managing costs, and ensuring the reliability of LLMs in production requires dedicated monitoring, observability, and maintenance strategies tailored to their unique characteristics. The scale, cost, and specific failure modes of LLMs demand more than standard application monitoring.

This chapter focuses on the practices required to keep production LLMs healthy and effective. You will learn how to:

• Define and track LLM-specific performance metrics, such as inference latency and token throughput.
• Monitor the underlying infrastructure, paying close attention to GPU utilization and memory consumption.
• Implement systems for tracking the significant operational costs associated with LLM training and serving.
• Detect data and concept drift specific to language models.
• Monitor LLM output quality, including technical approaches for identifying issues like hallucinations or undesirable content patterns.
• Establish feedback loops to gather data for model improvement and retraining triggers.
• Utilize logging and observability tools effectively within an LLMOps context.

We will cover the tools and techniques needed to gain visibility into your model's behavior, address issues proactively, and ensure its continued value over time.