Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
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Updated
Jul 16, 2024 - Jupyter Notebook
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Algorithms for outlier, adversarial and drift detection
nannyml: post-deployment data science in python
Curated list of open source tooling for data-centric AI on unstructured data.
⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
Frouros: an open-source Python library for drift detection in machine learning systems.
Toolkit for evaluating and monitoring AI models in clinical settings
Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.
A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀
In this repository, we will present techniques to detect covariate drift, and demonstrate how to incorporate your own custom drift detection algorithms and visualizations with SageMaker model monitor.
A comprehensive solution for monitoring your AI models in production
A ⚡️ Lightning.ai ⚡️ component for train and test data drift detection
Data Drift detection using auto encoders
Adversarial labeller is a sklearn compatible instance labelling tool for model selection under data drift.
Dataset shift with outlier scores
End to End Machine Learning Observability Project
Passively collect images for computer vision datasets on the edge.
Predicting the number of bicycles at rental stations.
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