Data management

In traditional MLOps, we often deal with data-intensive ML models. Training a neural network from scratch necessitates a significant volume of labeled data, and even the fine-tuning of a pre-trained model typically requires several hundred samples. We acknowledge that large datasets will inevitably contain imperfections, but data cleaning remains a critical part of the ML development process.

In the realm of LLMOps, the fine-tuning process shares similarities with that of MLOps. However, prompt engineering introduces a zero-shot or few-shot learning paradigm. This approach involves using a limited number of carefully selected samples, highlighting the need for high-quality, meticulously curated data rather than large volumes of potentially imperfect data. This shift emphasizes the importance of precision and quality in data selection for LLMOps.