MLflow Pilot Service for Helmholtz Researchers

As the field of Artificial Intelligence / Machine Learning (AI/ML) advances, managing and monitoring intelligent models during their lifecycle, also known as machine learning operations (MLOps), has become essential [1]. MLflow is an open-source platform [2] to assist AI/ML practitioners and teams in handling the AI/ML lifecycle, ensuring that each stage is manageable, traceable, and reproducible. MLflow v3 has many comprehensive features, including Experiment Tracking for parameter logging, metrics visualisation, and artifact storage; Model Registry for model versioning and lineage tracking; Datasets module for dataset management. It also features GenAI capabilities like LLM Observability (Tracing), Evaluation and Monitoring of GenAI applications, Prompt Management, and AI Gateway.

Our team provides MLflow instances within several EU projects, including AI4EOSC and iMagine, and upcoming FLUID-AI and EOSC-ARENA, covering use cases from various scientific domains. Recently, we also brought a new pilot instance for Helmholtz researchers [3]. The Helmholtz instance is coupled with Helmholtz ID/AAI and LSDF large storage and allows experiment and model sharing among MLflow users for collaborative development. ... mehrA major upgrade that also includes group-sharing capabilities is foreseen before June 2026. In this contribution, we are going to present the advantages of the MLflow platform for AI/ML researchers, share our experience in providing MLflow for various use-cases and supporting them with their workflows, and demonstrate the main features of the available Helmholtz pilot instance.

[1] Berberi, L., Kozlov, V., Nguyen, G. et al. Machine learning operations landscape: platforms and tools. Artif Intell Rev 58, 167 (2025). https://doi.org/10.1007/s10462-025-11164-3

[2] https://mlflow.org/ and https://mlflow.org/docs

[3] https://mlflow.scc.kit.edu and https://mlops.data.kit.edu