alibi-detect
Cypress
alibi-detect | Cypress | |
---|---|---|
9 | 174 | |
2,085 | 46,167 | |
1.6% | 0.4% | |
7.6 | 9.8 | |
12 days ago | 4 days ago | |
Python | JavaScript | |
GNU General Public License v3.0 or later | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
Cypress
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Simulating Internet Outage and Recovery using Cypress
In this blog post, we'll explore a Cypress test that replicates this scenario, utilizing the powerful intercept command to manipulate network requests and responses.
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Testing Defer Blocks in Angular with Cypress
Recently I came across this issue while triaging some issues at Cypress. (Shout out to MattiaMalandrone for creating an issue with clear instructions for how to reproduce). After quickly replicating the issue I sought after a solution which ultimately inspired me to write this article.
- Cypress changed older versions to block third-party plugins (ignoring lockfiles)
- Cypress can't open Tesla.com website
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What is Playwright?
While similar to Puppeteer, Cypress, and Selenium, there are some differences. Let’s find out what they are.
- Episode 23/37: ISR in Angular, Cypress & Playwright
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/Does Cypress Component Testing Work With Libraries
This questions was asked a while ago and pretty much went unanswered: https://github.com/cypress-io/cypress/issues/23677. If it doesn't work with libraries yet I will stop battling with it for now. If it doesn't work, what are you using to test libraries?
- Finally promising Web Testing solution
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Episode 23/27: NgRx 16.1 & Signal Store, Jest, Cypress, Nx
Cypress Release Notes
- Trouble/Weirdness with accessing aliased values in `this` context
What are some alternatives?
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Playwright - Playwright is a framework for Web Testing and Automation. It allows testing Chromium, Firefox and WebKit with a single API.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Detox - Gray box end-to-end testing and automation framework for mobile apps
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
jest - Delightful JavaScript Testing.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
kafka-test-helper - Utility library that simplify testing of Node.js components that interacts with Kafka broker.
river - 🌊 Online machine learning in Python
supertest - 🕷 Super-agent driven library for testing node.js HTTP servers using a fluent API. Maintained for @forwardemail, @ladjs, @spamscanner, @breejs, @cabinjs, and @lassjs.
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder
Sentry - Developer-first error tracking and performance monitoring