pyod
alibi-detect
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| pyod | alibi-detect | |
|---|---|---|
| 7 | 9 | |
| 7,928 | 2,074 | |
| - | 1.9% | |
| 7.7 | 7.6 | |
| 16 days ago | about 1 month ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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pyod
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A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
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Analyze defects and errors in the created images
PyOD
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Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
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[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
alibi-detect
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Numerous tools exist for detecting anomalies in time series data, but Alibi Detect stood out to me, particularly for its capabilities and its compatibility with both TensorFlow and PyTorch backends.
- Looking for recommendations to monitor / detect data drifts over time
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[D] Distributions to represent an Image Dataset
That is, to see whether a test image belongs in the distribution of the training images and to provide a routine for special cases. After a bit of reading Ive found that this is related to the field of drift detection in which I tried out alibi-detect . Whereby the training images are trained by an autoencoder and any subsequent drift will be flagged by the AE.
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[D] Which statistical test would you use to detect drift in a dataset of images?
Wasserstein distance is not very suitable for drift detection on most problems given that the sample complexity (and estimation error) scales with O(n^(-1/d)) with n the number of instances (100k-10m in your case) and d the feature dimension (192 in your case). More interesting will be to use for instance a detector based on the maximum mean discrepancy (MMD) with estimation error of O(n^(-1/2)). Notice the absence of the feature dimension here. You can find scalable implementations in Alibi Detect (disclosure: I am a contributor): MMD docs, image example. We just added the KeOps backend for the MMD detector to scale and speed up the drift detector further, so if you install from master, you can leverage this backend and easily scale the detector to 1mn instances on e.g. 1 RTX2080Ti GPU. Check this example for more info.
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- What Machine Learning model monitoring tools can you recommend?
- Ask HN: Who is hiring? (December 2021)
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[D] How do you deal with covariate shift and concept drift in production?
I work in this area and also contribute to outlier/drift detection library https://github.com/SeldonIO/alibi-detect. To tackle this type of problem, I would strongly encourage following a more principled, fundamentally (statistically) sound approach. So for instance measuring metrics such as the KL-divergence (or many other f-divergences) will not be that informative since it has a lot of undesirable properties for the problem at hand (in order to be informative requires already overlapping distributions P and Q, it is asymmetric, not a real distance metric, will not scale well with data dimensionality etc). So you should probably look at Integral Probability Metrics (IPMs) such as the Maximum Mean Discrepancy (MMD) instead which have much nicer behaviour to monitor drift. I highly recommend the Interpretable Comparison of Distributions and Models NeurIPS workshop talks for more in-depth background.
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[D] Is this a reasonable assumption in machine learning?
All of the above functionality and more can be easily used under a simple API in https://github.com/SeldonIO/alibi-detect.
What are some alternatives?
tods - TODS: An Automated Time-series Outlier Detection System
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
pycaret - An open-source, low-code machine learning library in Python
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis
river - 🌊 Online machine learning in Python
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder
pymiere - Python for Premiere pro
conductor - Conductor is a microservices orchestration engine.