project
Open-source platform for managing the machine learning lifecycle, including experiment tracking, model packaging, and a model registry used across ML pipelines.
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watchTowr says attackers are already pulling cloud credentials through MLflow's Tracking Server, and VulnCheck logged scanning against a FUXA path traversal a day later.
Perspective Coverage
5 publishersReality
The web tier's recovery target is 15 minutes; the on-prem ML pipeline's is 24 hours. A dev.to writeup covers the gap with a smaller model kept deployed in a second Azure region, and that stub runs continuously.
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The richer sync carries training metrics, evaluation results and lineage into the Model Package version so a reviewer can validate from the registry alone, but none of it happens until someone sets AutoModelRegistrationEnabled.
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The wrapper hashes code and output, sends only a digest out to be signed with RSA-PSS, and hands the reviewer a PDF. The cost of adopting it is a command prefix. The cost of believing it is trusting whoever holds the signing key.
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The retailer benchmarked several time series foundation models over 101 rolling cutoffs of its own catalogue, and the one it shipped now covers both the 12-week and 52-week horizons that two separate models used to split.
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One ModelTrainer and one ModelBuilder replace the SKLearn, PyTorch and XGBoost classes, with source directories synced at job launch. The container is now yours to supply.
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A GitHub advisory describes an unauthenticated, full-read SSRF in the default MLflow tracking server that reaches the cloud metadata endpoint and reflects the response back to the caller.
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CVE-2026-64849 lets anyone who can reach an MLflow tracking server make it fetch EC2 instance metadata and hand back the response. The fix is 3.15.0; the exposure is a default.
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