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SeldonIO
alibi-detect
Jupyter Notebook2020–2022
SeldonIO / alibi-detect

alibi-detect

Algorithms for outlier, adversarial and drift detection

Data & DatabasesLearning & ReferenceCreative & MediaLibraries & SDKs
Explore on GitHubRead Hacker News discussions
2Distinct people
3Mentions
0Explicit recommenders
2.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Algorithms for outlier, adversarial and drift detection

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2020-10-15 06:43:21 UTC to 2022-01-04 09:50:35 UTC.

012020-10-01 00:00:00 UTC to 2020-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsOct 20202020-11-01 00:00:00 UTC to 2020-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-12-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2021-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-02-01 00:00:00 UTC to 2021-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-03-01 00:00:00 UTC to 2021-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-04-01 00:00:00 UTC to 2021-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-05-01 00:00:00 UTC to 2021-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 20212021-06-01 00:00:00 UTC to 2021-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-07-01 00:00:00 UTC to 2021-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-08-01 00:00:00 UTC to 2021-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-09-01 00:00:00 UTC to 2021-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-10-01 00:00:00 UTC to 2021-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-11-01 00:00:00 UTC to 2021-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-12-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2022-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 2022

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Oct 20202020-10-01 00:00:00 UTC2020-11-01 00:00:00 UTC101
Nov 20202020-11-01 00:00:00 UTC2020-12-01 00:00:00 UTC000
Dec 20202020-12-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
Jan 20212021-01-01 00:00:00 UTC2021-02-01 00:00:00 UTC000
Feb 20212021-02-01 00:00:00 UTC2021-03-01 00:00:00 UTC000
Mar 20212021-03-01 00:00:00 UTC2021-04-01 00:00:00 UTC000
Apr 20212021-04-01 00:00:00 UTC2021-05-01 00:00:00 UTC000
May 20212021-05-01 00:00:00 UTC2021-06-01 00:00:00 UTC000
Jun 20212021-06-01 00:00:00 UTC2021-07-01 00:00:00 UTC000
Jul 20212021-07-01 00:00:00 UTC2021-08-01 00:00:00 UTC000
Aug 20212021-08-01 00:00:00 UTC2021-09-01 00:00:00 UTC000
Sep 20212021-09-01 00:00:00 UTC2021-10-01 00:00:00 UTC000
Oct 20212021-10-01 00:00:00 UTC2021-11-01 00:00:00 UTC000
Nov 20212021-11-01 00:00:00 UTC2021-12-01 00:00:00 UTC000
Dec 20212021-12-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
Jan 20222022-01-01 00:00:00 UTC2022-02-01 00:00:00 UTC101

What Hacker News says

3 sources
ivvv·Jan 4, 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/
In Ask HN: Who is hiring? (January 2022)
ivvv·Dec 2, 2021
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/
In Ask HN: Who is hiring? (December 2021)
kthejoker2·Oct 15, 2020
It's sad to see such "to hell with it" naivete (and the comments here with broad supporting evidence that data scientists are poor software developers. There are also a lot of DS people wrestling with the deeper challenges at the heart of MLOps and beyond just advocating sane team-based workflows, are looking at ways to harden and structure ML specific processes in the planning, design, build, and deploy phases. Sean Taylor at Lyft on choosing metrics - https://medium.com/@seanjtaylor/designing-and-evaluating-met... Test coverage of ML models and a good discussion on why and how ML code testing isn't the same as traditional testing - https://www.jeremyjordan.me/testing-ml/ A tool for Behavioral testing of NLP models - https://github.com/marcotcr/checklist How to be a 10x data scientist (spoiler: have a professional end to end workflow) -
Read the rest
https://towardsdatascience.com/how-to-be-a-10x-data-scientis... Ml-specific technical debt, how to measure it, some ideas on how to handle it, this paper has spawned a lot of great tools - http://papers.nips.cc/paper/5656-hidden-technical-debt-in-ma... Adversarial instance and outlier detection - https://github.com/SeldonIO/alibi-detect And of course a whole suite of tools and research beingn done on data, pipeline, and model management, optimizing deployment and serving, explainability and interpretability, all in the service of applying enterprise-ready, engineering-grade rigor to data science. And then you read something like this and want to tear your hair out.
In Why software engineering processes and tools don’t work for machine learning

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