LLMOps workflow overview

LLMOps represent the culmination of advanced machine learning practices tailored specifically for LLMs. It encapsulates an end-to-end process that ensures these models are not only built with the highest level of technical expertise but are also deployed and managed in ways that maximize their utility and adhere to ethical standards.

Step-by-step overview

This LLMOps life cycle encompasses several distinct phases, each critical to the successful deployment and operation of LLMs.

Data selection and preparation

This forms the basis for the performance and effectiveness of LLMs. Datasets must be expansive to ensure broad coverage, diverse to capture various linguistic nuances, and inclusive to reflect a wide array of language use cases. Such well-rounded datasets are a key factor for their functionality and accuracy.

Data quality directly impacts the model’s performance. Rigorous data cleaning and preprocessing are essential, entailing the...