What’s LLM Observability ? Latest tools to look out for

2024 is looking to be the year where a lot of applied Large Language Models (LLMs) from enterprise companies, other than the creators of the foundation LLMs, are going to come out of the Proof of Concept (POC) phase to actually being used by their customers. It’s gonna be a year of trial and error, some with fantastic user metrics, some with less desirable ones, but mostly all may come out with some unbelievable insights. But we all know – what’s not measured, is not gonna be improved. So how do we measure and quantify how our deployed LLMs are doing ? Especially with significant upfront and hidden costs of training, fine-tuning and maintaining these LLMs, it’s high time AI product leaders spend a significant time and effort in post model deployment metrics and insights. Historically, this phase has been the one with the least effort and time spent on – but the more we measure earlier, the more Return On Investments (ROI) we can gain.

What is ML / LLM Observability ?

Observability in the context of Machine Learning (ML) and Large Language Models (LLMs) refers to the ability to understand, monitor, and gain insights into the internal workings and behaviors of these models during training, validation, and inference. It involves tracking various metrics, logging relevant information, and visualizing key aspects to ensure that these systems are performing as expected and to troubleshoot issues when they arise. ML and LLM observability is crucial for maintaining model performance, understanding model behavior, and ensuring the reliability and effectiveness of these systems.

Here are some aspects of ML and LLM observability:

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