A Guide to LLMOps: Large Language Model (LLM) Operations
Navigating the management, deployment, and optimization of LLMs and how to effectively enhance their performance.
The growing complexity and scale of large language models (LLMs) pose unique challenges that traditional Machine Learning Operations (MLOps) often need help managing. Large Language Model Operations (LLMOps) have emerged to address these, providing a tailored framework designed to navigate the intricate requirements of developing, managing, and operating LLMs.
LLMOps provides a structured methodology that enables organizations to systematically evaluate and harness the potential of LLMs quickly and safely. Throughout the model's lifecycle, LLMOps practices are a collaborative bridge for various stakeholders, from data engineers to data scientists and ML engineers.
In this paper, we dive deeper into LLMOps, distinguish it from MLOps, and guide you through their key components, challenges, best practices and the promising future it paves for operations with LLMs.
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WhyLabs gives organizations the power to control the health of AI-enabled applications by surfacing and preventing undesirable AI behavior, including leakage of sensitive data, the presence of malicious prompts, toxic responses, problematic topics, hallucinations, and jailbreak attempts. Incubated at the Allen Institute for AI, WhyLabs is a privately held, venture-funded company based in Seattle.
