MLflow Status — Is MLflow Down?

Operational

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Service Status

MLflow

Operational119ms

24-Hour Uptime

100%

Operational

Degraded

Outage

No data

Latency (24h)

Recent Incidents

No incidents in the past 30 days

Common Issues with MLflow

See detailed troubleshooting guides for the most common MLflow errors:

API Error (500 / 502 / 503)

MLflow is returning API errors (HTTP 500, 502, or 503), indicating the platform's backend is experiencing issues. MLOps platforms handle complex workloads — model training, experiment tracking, pipeline orchestration — making server errors particularly disruptive to active workflows.

Pipeline / Training Run Failed

Your ML pipeline or training run on MLflow has failed unexpectedly. This can be caused by platform-side issues (compute unavailability, orchestration bugs) or configuration problems on your end. Distinguishing between the two is key to a fast resolution.

Model Registry / Artifact Store Error

You cannot push, pull, or access model artifacts in MLflow's model registry. This blocks model deployment and versioning workflows. Registry errors are often caused by storage backend issues or permission problems.

About MLflow

Open-source platform for ML lifecycle management

Official Website

Category

Monitored Surfaces

MLflow

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