mlrun/mlrun

What it solves

MLRun is an AI orchestration platform designed to reduce the engineering effort and time required to move AI and generative AI applications from development to production. It breaks down silos between data, ML, and DevOps teams by automating the delivery of data, ML pipelines, and online applications across their entire lifecycle.

How it works

MLRun provides a central control and metadata layer to manage project assets like functions, jobs, and secrets. It uses a serverless architecture (via Nuclio) to deploy scalable real-time serving pipelines. The platform integrates data management (including a Feature Store for offline and online features) with automated ML pipelines for training, evaluation, and deployment. It also includes built-in observability to monitor model behavior, drift, and resource usage without requiring complex code instrumentation.

Who it’s for

It is intended for data scientists, ML engineers, and MLOps teams who need to build, scale, and monitor continuous AI applications in an enterprise environment.

Highlights

• End-to-End Orchestration: Manages the full lifecycle from data ingestion and preprocessing to model training, evaluation, and real-time serving.
• Gen AI Support: Specific workflows for RAG, LLM fine-tuning, and LLM evaluation.
• Serverless Deployment: Uses real-time auto-scaling serverless functions for production-grade application pipelines.
• Integrated Feature Store: Automates the collection, transformation, and serving of data features for both development and production.
• Built-in Observability: Native monitoring for data drift, model performance, and resource usage with integrated alerting.