Getting Started with LLMOps: Building Better AI Applications

OpenAI's ChatGPT has sparked a surge of interest in large language models (LLMs) among corporations, leading to increased demand for technology vendors that support LLM operations (LLMOps). These vendors provide comprehensive workflows for developing, fine-tuning, and deploying LLMs into production environments. Sage Elliott, a machine learning engineer at Union.ai, discussed deploying and managing LLMs during a recent Unstructured Data Meetup, focusing on ensuring the reliability and scalability of LLM applications in production settings. LLMOps stands for Large Language Model Operations, which are analogous to MLOps but specifically for large language models (LLMs). MLOps (Machine Learning Operations) refers to the practices and tools used to efficiently deploy and maintain machine learning models in production environments. It is an extension of DevOps (Development and Operations), which integrates application development and operations into a cohesive process. Continuous Integration/Continuous Deployment (CI/CD) is one of the core principles of LLMOps, automating the LLM application development lifecycle. Continuous integration (CI) involves automatically taking application updates and merging them with the main branch, while continuous delivery/deployment (CD) refers to the process of automatically deploying changes to the application in a production environment after integration and validation. LLMOps is essential for production-level AI applications, with the exact infrastructure dependent on the application's needs. Integrating LLMOps into your AI application offers benefits such as resource management and scalability, model updating and improvements, and ethical and responsible AI practices. A simplified LLMOps pipeline includes elements like Sys Prompt, Model, Guardrail, Data Store, Monitor, and CI/CD Orchestrator. The HuggingFace Spaces platform streamlines the shipping of your model into production, offering low-cost cloud GPUs to power LLMs. To get started with LLMOps, follow a simple three-step philosophy: ship the model, monitor its performance, and improve it based on insights gained from monitoring. Tools like LangKit, Ragas, Continuous Eval, TruLens-Eval, LlamaIndex, Phoenix, DeepEval, LangSmith, and OpenAI Evals can help evaluate LLM applications.