| Name | gate-drift JSON |
| Version |
0.1.5
JSON |
| download |
| home_page | |
| Summary | Data drift detection tool for machine learning pipelines. |
| upload_time | 2023-04-28 17:33:46 |
| maintainer | |
| docs_url | None |
| author | Shreya Shankar |
| requires_python | >=3.8,<4.0 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
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# GATE: Data Drift Detection for Machine Learning Pipelines
[](https://github.com/dm4ml/gate/actions?query=workflow:"gate")
[](https://github.com/dm4ml/gate/actions?query=workflow:"lint")
[](https://github.com/psf/black)
GATE is a Python module that detects drift in partitions of data. GATE computes partition summaries, which are then fed into an anomaly detection algorithm to detect whether a new partition is anomalous. This minimizes false positive alerts when detecting drift in machine learning (ML) pipelines, where there may be many features and prediction columns.
### Support for Embeddings
We now support drift detection on embeddings, in addition to structured data. GATE considers _both_ the structured data and the embeddings when computing partition summaries and detecting drift. Check out the [embeddings page](./embedding) for a walkthrough of how to use GATE with embeddings.
## Installation
GATE is available on PyPI and can be installed with pip:
```bash
pip install gate-drift
```
Note that GATE requires Python 3.8 or higher.
## Usage
GATE is designed to be used with [Pandas](https://pandas.pydata.org/) dataframes. Check out the [documentation](https://dm4ml.github.io/gate/) for a walkthrough of how to use GATE.
## Research Contributions
GATE was developed and is maintained by researchers at the UC Berkeley [EPIC Lab](https://epic.berkeley.edu/).
An initial version of GATE was developed as part of a collaboration with Meta, and the research paper, "Moving Fast With Broken Data" by Shankar et al., is available on [arXiv](https://arxiv.org/abs/2303.06094). This module slightly differs from the original implementation, but the core ideas around partition summaries and anomaly detection are the same.
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