This Python package provides a robust framework for implementing LLMOps, targeting engineers and researchers looking to streamline the development, deployment, and monitoring of Large Language Models. It offers a structured approach to MLOps best practices tailored for LLM use cases, aiming to enhance flexibility, productivity, and reliability in LLM-centric projects.

How It Works

The package employs a modular design, leveraging tools like MLflow for model registry and tracking, and Lit-serve for endpoint deployment. It emphasizes rigorous evaluation through synthetic QA datasets and incorporates guardrails for PII and topic censoring. The architecture follows a pattern of logging and evaluating LLM chains in MLflow, promoting high-performing models to production, and utilizing MLflow Traces for continuous monitoring with LLM-as-a-judge evaluations.

Quick Start & Requirements

• Install: Clone the repository and run poetry install.
• Prerequisites: Python >= 3.10, Poetry >= 1.8.2. Requires LLM provider credentials (e.g., AWS environment variables for Bedrock).
• Setup: Estimated setup time is minimal after cloning and installing dependencies.
• Docs: MLOps Coding Course, Cookiecutter MLOps Package

Highlighted Details

• RAG Evaluation: Uses synthetic QA datasets for baseline performance evaluation of RAG systems.
• Model Registry: MLflow is used to log, evaluate, and promote LLM chains based on performance against a RAG evaluation baseline.
• Guardrails: Includes configuration for PII and topic censoring using Guardrails AI.
• Endpoint Deployment: Utilizes Lit-serve (built on FastAPI) for flexible LLM serving, with templates for AWS Fargate.

Maintenance & Community

The project appears to be a personal initiative by callmesora. Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state a license. It mentions leveraging resources from other projects, implying potential licensing considerations. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is presented as a template and variation of existing resources, suggesting it may require significant adaptation for specific production environments. Details on community support, active maintenance, or formal testing against various LLM providers are not provided.