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SeldonIO
seldon-server
Java2015–2017
SeldonIO / seldon-server

seldon-server

Machine Learning Platform and Recommendation Engine built on Kubernetes

3Distinct people
6Mentions
0Explicit recommenders
1.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Machine Learning Platform and Recommendation Engine built on Kubernetes

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

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:01:02 AM 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 month6 dated mentions

Observed mentions: 2015-03-17 04:13:38 UTC to 2017-05-16 08:33:53 UTC.

01232015-03-01 00:00:00 UTC to 2015-04-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsMar 20152015-04-01 00:00:00 UTC to 2015-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-05-01 00:00:00 UTC to 2015-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-06-01 00:00:00 UTC to 2015-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-07-01 00:00:00 UTC to 2015-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-08-01 00:00:00 UTC to 2015-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-09-01 00:00:00 UTC to 2015-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-10-01 00:00:00 UTC to 2015-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-11-01 00:00:00 UTC to 2015-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-12-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-01-01 00:00:00 UTC to 2016-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-02-01 00:00:00 UTC to 2016-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-03-01 00:00:00 UTC to 2016-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-04-01 00:00:00 UTC to 2016-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsApr 20162016-05-01 00:00:00 UTC to 2016-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-06-01 00:00:00 UTC to 2016-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-07-01 00:00:00 UTC to 2016-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-08-01 00:00:00 UTC to 2016-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-09-01 00:00:00 UTC to 2016-10-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2016-10-01 00:00:00 UTC to 2016-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-11-01 00:00:00 UTC to 2016-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-12-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2017-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-02-01 00:00:00 UTC to 2017-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-03-01 00:00:00 UTC to 2017-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-04-01 00:00:00 UTC to 2017-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-05-01 00:00:00 UTC to 2017-06-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 commentsMay 2017

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
Mar 20152015-03-01 00:00:00 UTC2015-04-01 00:00:00 UTC211
Apr 20152015-04-01 00:00:00 UTC2015-05-01 00:00:00 UTC000
May 20152015-05-01 00:00:00 UTC2015-06-01 00:00:00 UTC000
Jun 20152015-06-01 00:00:00 UTC2015-07-01 00:00:00 UTC000
Jul 20152015-07-01 00:00:00 UTC2015-08-01 00:00:00 UTC000
Aug 20152015-08-01 00:00:00 UTC2015-09-01 00:00:00 UTC000
Sep 20152015-09-01 00:00:00 UTC2015-10-01 00:00:00 UTC000
Oct 20152015-10-01 00:00:00 UTC2015-11-01 00:00:00 UTC000
Nov 20152015-11-01 00:00:00 UTC2015-12-01 00:00:00 UTC000
Dec 20152015-12-01 00:00:00 UTC2016-01-01 00:00:00 UTC000
Jan 20162016-01-01 00:00:00 UTC2016-02-01 00:00:00 UTC000
Feb 20162016-02-01 00:00:00 UTC2016-03-01 00:00:00 UTC000
Mar 20162016-03-01 00:00:00 UTC2016-04-01 00:00:00 UTC000
Apr 20162016-04-01 00:00:00 UTC2016-05-01 00:00:00 UTC000
May 20162016-05-01 00:00:00 UTC2016-06-01 00:00:00 UTC000
Jun 20162016-06-01 00:00:00 UTC2016-07-01 00:00:00 UTC000
Jul 20162016-07-01 00:00:00 UTC2016-08-01 00:00:00 UTC000
Aug 20162016-08-01 00:00:00 UTC2016-09-01 00:00:00 UTC000
Sep 20162016-09-01 00:00:00 UTC2016-10-01 00:00:00 UTC110
Oct 20162016-10-01 00:00:00 UTC2016-11-01 00:00:00 UTC000
Nov 20162016-11-01 00:00:00 UTC2016-12-01 00:00:00 UTC000
Dec 20162016-12-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
Jan 20172017-01-01 00:00:00 UTC2017-02-01 00:00:00 UTC000
Feb 20172017-02-01 00:00:00 UTC2017-03-01 00:00:00 UTC000
Mar 20172017-03-01 00:00:00 UTC2017-04-01 00:00:00 UTC000
Apr 20172017-04-01 00:00:00 UTC2017-05-01 00:00:00 UTC000
May 20172017-05-01 00:00:00 UTC2017-06-01 00:00:00 UTC321

What Hacker News says

3 sources
Fantastic resource - thanks, a16z! Do you accept pull requests? I'd like to make a suggestion for the software and services page: http://aiplaybook.a16z.com/docs/reference/software Also, I think there's an emerging category outside of MLaaS that focuses on ML/DL deployment (ops). Here's a quick intro: Seldon's mission is to help people predict and shape the future with machine learning. Our products increase efficiency and speed up the time to value for data scientists: Seldon Core - a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure). Seldon is framework agnostic, it supports mod
Read the rest
els built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool — it even supports models built with commercial tools and services where the model is exportable. Fork Seldon on Github: https://github.com/SeldonIO/seldon-server/ Seldon Deploy - in development - a machine learning deployment platform for data science teams. Features include workflows with approvals and audit trails, advanced experiments, continuous integration and model explanations. Seldon was part of the Barclays Techstars '16 and we’re working with customers in banking, technology and media sectors. Disclosure: I’m CEO at Seldon.
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