Show HN: LUML – an open source (Apache 2.0) MLOps/LLMOps platform (github.com)

LUML has launched as an open-source MLOps/LLMOps platform designed to streamline the machine learning lifecycle from experimentation to deployment. Key features include experiment tracking, model registry, and flexible deployment options, all while ensuring that users maintain control over their data and computing resources. This architecture prioritizes resource isolation, allowing users to operate separate storage and compute infrastructures while LUML manages orchestration and access control. The platform is built on the AIOps framework, integrating MLOps with LLMOps and AgentOps to create a cohesive operational environment for various AI workloads. The significance of LUML lies in its approach to managing AI resources through Organizations, Orbits, Satellites, and Buckets, enabling efficient collaboration while securely handling data. It utilizes a client-side data transfer model, safeguarding user autonomy by avoiding intermediary handling of files. Additionally, features like Experiment Snapshots and LLM Tracing enhance transparency in model performance and execution. By providing an integrated environment for both automating model building and in-browser experimentation, LUML aims to simplify the complexities of machine learning workflows for developers, ultimately fostering innovation and efficiency within the AI/ML community.