Deploying LLM apps
Given the increasing use of LLMs in various sectors, it’s imperative to understand how to effectively deploy LangChain and LangGraph applications into production. Deployment services and frameworks can help to scale the technical hurdles, with multiple approaches depending on your specific requirements.
Before proceeding with deployment specifics, it’s worth clarifying that MLOps refers to a set of practices and tools designed to streamline and automate the development, deployment, and maintenance of ML systems. These practices provide the operational framework for LLM applications. While specialized terms like LLMOps, LMOps, and Foundational Model Orchestration (FOMO) exist for language model operations, we’ll use the more established term MLOps throughout this chapter to refer to the practices of deploying, monitoring, and maintaining LLM applications in production.
Deploying generative AI applications to production...
