From Uncertainty to Certainty: Strategies for Deterministic LLMOps

How do you ensure reliability on a non-deterministic system where the outcome of a process is not guaranteed to be the same every time? Large Language Models (LLMs) exemplify this unpredictability, creating hurdles in mission-critical business applications that demand consistency. This presentation focuses on the practical implementation of deterministic strategies, introducing a qualitative method to assess, evaluate, and refine Large Language Models. By determining factors such as toxicity, latency, cost, and bias, we will explore how to use MLflow and Dataiku to establish monitoring mechanisms and track these metrics over time. Discover how to reassure stakeholders and cultivate trust in LLMs by addressing their unpredictability. The session will give you a strategic advantage in deploying and operationalizing LLMs across diverse applications, empowering control amid uncertainty.