🚀Deployment and Monitoring

Deployment

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. The sheer scale of these models requires high-performance computing resources, such as specialized GPUs with large amounts of memory. Additionally, the size of these models can make them computationally expensive, which can significantly increase training and inference times.

Monitoring

LLMs can exhibit significant changes in their output from one release to another. For instance, OpenAI has updated its models to curtail the generation of inappropriate content, such as hate speech. This has led to the widespread use of phrases like "as an AI language model" by numerous bots on Twitter.

This underscores the necessity for continuous monitoring when deploying LLM-powered applications, as alterations in the underlying API model can significantly influence the application's behavior.

In response to this need, tools specifically designed for LLM monitoring have begun to emerge, including platforms like Whylabs and HumanLoop. These solutions provide developers with the means to track and manage changes in LLM behavior, ensuring that their applications remain robust and reliable over time.