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hmert
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Oct 15, 2025
README.md
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| * [PerpetualBooster](https://github.com/perpetual-ml/perpetual) | ||
| * [JAX](https://github.com/google/jax) | ||
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| ### Model Evaluation & Monitoring |
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please remove this, it more than a link.
hmert
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Oct 21, 2025
README.md
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| #### Evidently AI | ||
| [Evidently AI](https://www.evidentlyai.com/) - An open-source framework for evaluating, monitoring, and analyzing machine learning models in production. Evidently provides interactive reports and dashboards for data drift, model performance, and data quality tracking. | ||
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| **Key Features:** | ||
| - **Comprehensive reports:** Analyze model quality, data drift, and target drift with visual, shareable reports. | ||
| - **Production monitoring:** Continuously track metrics and detect performance degradation. | ||
| - **Integration-friendly:** Works seamlessly with Jupyter notebooks, pipelines, and production monitoring tools. | ||
| - **Focus on explainability:** Helps data science teams understand changes in data and model behavior over time. | ||
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| **Why it matters:** | ||
| Monitoring model performance is crucial for reliable AI systems. Evidently AI simplifies this process, making continuous evaluation and transparency easy to integrate into ML workflows. | ||
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please remove all of this, we only provide link of the content
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This pull request adds a new section to the
README.mdfocused on model evaluation and monitoring, specifically highlighting the Evidently AI tool. This improves the documentation by guiding users to resources for monitoring and evaluating machine learning models in production, which is an essential aspect of deploying reliable AI systems.Additions to documentation on model evaluation and monitoring: