https://github.com/mpolinowski/ray-serve-model
Using Ray Serve for ML Model Serving
https://github.com/mpolinowski/ray-serve-model
consensus model-serving python ray
Last synced: 7 months ago
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Using Ray Serve for ML Model Serving
- Host: GitHub
- URL: https://github.com/mpolinowski/ray-serve-model
- Owner: mpolinowski
- Created: 2023-02-01T06:09:07.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-02-01T06:15:37.000Z (about 3 years ago)
- Last Synced: 2025-03-23T13:14:24.445Z (11 months ago)
- Topics: consensus, model-serving, python, ray
- Language: Python
- Homepage: https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/AIOps/2023-01-31-python-ray-model-serving/2023-01-31
- Size: 1.47 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Python Ray Model Serving
> Source: [Scaling Python with Ray](https://github.com/scalingpythonml/scaling-python-with-ray)
I earlier looked at [Wine Dataset](https://github.com/mpolinowski/scikit-wine-quality) and used a couple of __SciKit Learn Classifier__ to fit different models to this data to make a binary classification _"Is this wine any good?"_ based on a set of features provided by the dataset. Let's see how we can provision a prediction API based on the trained models using Ray.