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  • MLServer
  • Getting Started
  • User Guide
    • Content Types (and Codecs)
    • OpenAPI Support
    • Parallel Inference
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    • Metrics
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      • Seldon Core
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  • Reference
    • MLServer Settings
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  • Examples
    • Serving Scikit-Learn models
    • Serving XGBoost models
    • Serving LightGBM models
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    • Serving a custom model
    • Serving Alibi-Detect models
    • Serving HuggingFace Transformer Models
    • Multi-Model Serving
    • Model Repository API
    • Content Type Decoding
    • Custom Conda environments in MLServer
    • Serving a custom model with JSON serialization
    • Serving models through Kafka
    • Streaming
    • Deploying a Custom Tensorflow Model with MLServer and Seldon Core
  • Changelog
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  1. Reference
  2. Python API

Metrics

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Last updated 6 months ago

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The MLServer package exposes a set of methods that let you register and track custom metrics. This can be used within your own . To learn more about how to expose custom metrics, check out the .

.. automodule:: mlserver
   :members: register, log
custom inference runtimes
metrics usage guide