Drift Lens Demo
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Updated
Apr 15, 2024 - Python
Drift Lens Demo
An ML monitoring framework, applied to an attrition risk assessment system.
A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
A tiny framework to perform adversarial validation of your training and test data.
Adversarial labeller is a sklearn compatible instance labelling tool for model selection under data drift.
Passively collect images for computer vision datasets on the edge.
A ⚡️ Lightning.ai ⚡️ component for train and test data drift detection
Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.
Toolkit for evaluating and monitoring AI models in clinical settings
Frouros: an open-source Python library for drift detection in machine learning systems.
nannyml: post-deployment data science in python
Algorithms for outlier, adversarial and drift detection
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.
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