(mahout) branch main updated: [Website] Add Books/Talks, Reference Reading and Professional Support pages; split Powered By into Commercial and Academic Use (#1497)

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     new e6ba290ea [Website] Add Books/Talks, Reference Reading and Professional Support pages; split Powered By into Commercial and Academic Use (#1497)
e6ba290ea is described below

commit e6ba290ead032dbf84e70dc2469df648981e4298
Author: Jie-Kai Chang <ji...@apache.org>
AuthorDate: Wed Sep 23 02:21:54 2026 +0800

    [Website] Add Books/Talks, Reference Reading and Professional Support pages; split Powered By into Commercial and Academic Use (#1497)
    
    Signed-off-by: 400Ping <ji...@apache.org>
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 docs/learning/books-tutorials-and-talks.md         | 156 +++++++++++++++++++++
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 docs/learning/reference-reading.md                 |  79 +++++++++++
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 .../learning/books-tutorials-and-talks.md          | 156 +++++++++++++++++++++
 .../version-0.6/learning/professional-support.md   |  55 ++++++++
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diff --git a/docs/index.md b/docs/index.md
index c42c85c46..06857517b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -38,6 +38,9 @@ New here? Start with [Getting Started](./qumat/getting-started), which covers in
 - [PQC Guides](./advanced) - Developer's guide and gap analysis for parameterized quantum circuits
 - [Quantum Computing Primer](./learning/quantum-computing-primer) - A ten-chapter introduction to quantum computing using Qumat
 - [Research Papers](./learning/papers) - Papers and publications related to Qumat and quantum computing
+- [Books, Tutorials and Talks](./learning/books-tutorials-and-talks) - Reading, articles, lectures, and conference talks
+- [Reference Reading](./learning/reference-reading) - Background material on linear algebra, statistics, and machine learning
+- [Professional Support](./learning/professional-support) - People and companies offering Mahout support and talks
 
 ## Community
 
diff --git a/docs/learning/books-tutorials-and-talks.md b/docs/learning/books-tutorials-and-talks.md
new file mode 100644
index 000000000..78ae7d6b4
--- /dev/null
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@@ -0,0 +1,156 @@
+---
+title: Books, Tutorials and Talks
+sidebar_label: Books, Tutorials & Talks
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Books, Tutorials and Talks
+
+This page collects talks, tutorials, articles, books, slides, and lectures about Apache Mahout, from the current Qumat and QDP work back to the Hadoop-era releases. No endorsements are implied or given. To add an entry, open a pull request against this page or email dev@mahout.apache.org.
+
+For general background on linear algebra, statistics, and machine learning, see [Reference Reading](./reference-reading).
+
+## Books
+
+### Qumat and quantum computing
+
+* [Quantum Computing Primer](./quantum-computing-primer/) - A ten-chapter introduction to quantum computing using Qumat, maintained in these docs.
+* [Research Papers](./papers/) - Papers and publications related to Qumat and quantum machine learning.
+
+### Mahout specific
+
+* [Apache Mahout: Beyond MapReduce](http://www.weatheringthroughtechdays.com/2016/02/mahout-samsara-book-is-out.html) by Dmitriy Lyubimov and Andrew Palumbo published Feb 2016. Covers new features in Mahout "Samsara" releases (0.10, 0.11+).
+* [Apache Mahout cookbook](http://www.packtpub.com/apache-mahout-cookbook/book)- Book by Piero Giacomelli published Dec 2013 by Packtpub.
+* [Mahout in Action](http://www.manning.com/owen/) - Book by Sean Owen, Robin Anil, Ted Dunning and Ellen Friedman published Oct 2011 by Manning Publications.
+* [Taming Text](http://www.manning.com/ingersoll/) - By Grant Ingersoll and Tom Morton, published by Manning Publications. Will have some Mahout coverage, but by no means as complete as Mahout in Action.
+
+### Engineering oriented machine learning books
+
+* [Collective Intelligence in Action](http://www.amazon.com/Collective-Intelligence-Action-Satnam-Alag/dp/1933988312/ref=pd_bbs_sr_3?ie=UTF8&s=books&qid=1214545249&sr=1-3)
+* [Programming Collective Intelligence](http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325/ref=pd_bbs_sr_1/104-1017533-9408723?ie=UTF8&s=books&qid=1214593516&sr=1-1)
+* [Algorithms of the Intelligent Web](http://www.amazon.com/Algorithms-Intelligent-Web-Haralambos-Marmanis/dp/1933988665/ref=sr_1_1?s=books&ie=UTF8&qid=1298005918&sr=1-1)
+
+### Scientific background
+
+* [Data Mining: Practical Machine Learning Tools and Techniques](http://www.cs.waikato.ac.nz/~ml/weka/book.html)
+* [Introduction to Information Retrieval](http://www-nlp.stanford.edu/IR-book/)
+* [Machine Learning](http://www.amazon.com/Machine-Learning-Mcgraw-Hill-International-Edit/dp/0071154671/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1214593709&sr=8-1)
+* [Pattern Recognition and Machine Learning (Information Science and Statistics)](http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=pd_bbs_sr_2?ie=UTF8&s=books&qid=1214593709&sr=8-2)
+
+## News, Articles and Tutorials
+
+### Qumat and QDP
+
+* [Introducing QuMat v0.6.0](/blog/2026/06/01/Qumat-0.6.0-Release) - AMD ROCm support, six QDP encodings with CUDA/ROCm parity, and real-workload benchmarks, June 2026
+* [QuMat v0.5.0 release](/blog/2026/02/02/Qumat-0.5.0-Release) - First public QDP proof of concept for GPU state preparation, February 2026
+* [QuMat v0.4.0 release](/blog/2025/04/17/Qumat-0.4.0-Release) - April 2025
+* [Getting Started with Qumat](../qumat/getting-started) and [Getting Started with QDP](../qdp/getting-started) - The official quickstarts
+
+### Mahout Classic
+
+* [Mahout 0.10.x: first Mahout release as a programming environment](http://www.weatheringthroughtechdays.com/2015/04/mahout-010x-first-mahout-release-as.html)   
+* [Comparing Document Classification Functions of Lucene and Mahout](http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html)
+* [Apache Mahout: Scalable Machine Learning for Everyone](http://www.ibm.com/developerworks/java/library/j-mahout-scaling/)
+* [How to build a spam filter server with Mahout](http://emmaespina.wordpress.com/2011/04/26/ham-spam-and-elephants-or-how-to-build-a-spam-filter-server-with-mahout/) - Applying classification on a live server - April 2011
+* [Deploying a massively scalable recommender system with Apache Mahout](http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/) - Blogpost of Sebastian Schelter in April 2011
+* [Apache Mahout & the commoditization of machine learning](http://www.redmonk.com/cote/2010/11/04/makeall013/) - Podcast interview with Grant Ingersoll at ApacheCon 2010
+* [Apache Mahout 0.4 mit neuen Algorithmen](http://isabel-drost.de/hadoop/slides/devoxx.pdf) - published after the 0.4 release by heise Open/ Developer, November 2010
+* [Mahout on InfoQ](http://www.infoq.com/news/2009/04/mahout) - Interview with Grant Ingersoll on InfoQ
+* [Mahout in the Cloudera weblog](http://www.cloudera.com/blog/2009/04/21/hadoop-uk-user-group-meeting/) - published after the Hadoop user group UK.
+* [Mahout in the Drools weblog](http://blog.athico.com/2008/08/machine-learning-and-apache-mahout.html) - Michael Neale published an article on Mahout in the drools weblog
+* [Introducing Apache Mahout](https://www.ibm.com/developerworks/java/library/j-mahout/index.html) - Grant Ingersoll - Intro to Apache Mahout focused on clustering, classification and collaborative filtering. Japanese translation available at: [http://www.ibm.com/developerworks/jp/java/library/j-mahout/](http://www.ibm.com/developerworks/jp/java/library/j-mahout/)
+* [Flexible Collaborative Filtering In Java With Mahout Taste](http://philippeadjiman.com/blog/2009/11/11/flexible-collaborative-filtering-in-java-with-mahout-taste/) - Philippe Adjiman - Quick starting guide on how to use the collaborative filtering package of Mahout (called Taste) to quickly and flexibly create, test and compare tailored recommendation engines.
+* [Integrating Mahout with Lucene and Solr](http://www.lucidimagination.com/blog/2010/03/16/integrating-apache-mahout-with-apache-lucene-and-solr-part-i-of-3/) Three part series on ways to integrate Mahout with Lucene and Solr
+* [Mahout Item Recommender Tutorial using Java and Eclipse](https://www.youtube.com/watch?v=yD40rVKUwPI) - YouTube video tutorial by Steve Cook
+
+## Coursework/Lectures
+
+* [http://videolectures.net/mlss05us_chicago/](http://videolectures.net/mlss05us_chicago/)
+* [http://videolectures.net/mlas06_pittsburgh/](http://videolectures.net/mlas06_pittsburgh/)
+* [Stanford Lectures on Machine Learning by Andrew Ng](http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1)
+* [CMU@Qatar Introduction to Mahout lecture](https://docs.google.com/open?id=0ByhGL2_SCeitMDQ3OTczNjItM2ZjYi00ZDg5LWE0MzItZGQxODQ5NzkzYjNj)
+
+## Talks
+
+In reverse chronological order, so that most recent talks are at the top.
+
+### Qumat and QDP
+
+* [Accelerating Quantum Machine Learning: Building a GPU-Accelerated Data Plane in Apache Mahout](https://asia.communityovercode.org/sessions/general-1194670.html) - Jie-Kai Chang and Guan-Hua Wen at Community Over Code Asia 2026, Beijing, August 8, 2026.
+* [Introducing Qumat!](https://mirrors.dotsrc.org/fosdem/2025/k4401/fosdem-2025-5298-introducing-qumat-an-apache-mahout-joint-.av1.webm) - FOSDEM 2025, Brussels, February 2025.
+* [QuMat: Apache Mahout's Quantum Computing Interface](https://www.youtube.com/watch?v=tgFaUL1wYhY) - FOSSY 2024, Portland, August 2024.
+
+### Mahout Classic
+
+* Distributed Machine Learning with Apache Mahout - Suneel Marthi at Apache Big Data North America, Vancouver, Canada, May 11, 2016 and MapR Washington DC Big Data Everywhere, Tysons, VA, June 2 2016
+* [Declarative Machine Learning with the Samsara DSL](http://www.slideshare.net/FlinkForward/sebastian-schelter-distributed-machine-learing-with-the-samsara-dsl) Sebastian Schelter at Flink Forward Conference, Berlin Germany, October 2015.
+* [Bringing Algebraic Semantics to Mahout](http://www.slideshare.net/sscdotopen/bringing-algebraic-semantics-to-mahout) Sebastian Schelter at HPI Infolunch, Potsdam Germany, May 2014
+* Mahout Spark and Scala bindings: Bringing Algebraic Semantics ([slides](http://www.slideshare.net/DmitriyLyubimov/mahout-scala-and-spark-bindings)/[video](http://youtu.be/h9dpmvNW1Dw)) - Dmitriy Lyubimov at Mahout Meetup, April 17, 2014. 
+* Mahout Future Directions - Ted Dunning, Suneel Marthi, Sebastian Schelter at Hadoop Summit Europe 2014, Amsterdam, April 3, 2014
+* Building Recommender Systems for Mere-Mortals - Sebastian Schelter at Researchgate Developer Day, Berlin, November 2013
+* Recommendations with Apache Mahout - Sebastian Schelter at IBM Almaden Research Center, San Jose, September 2013
+* [Next Directions in Mahout’s Recommenders](http://de.slideshare.net/sscdotopen/next-directions-in-mahouts-recommenders) - Sebastian Schelter at Bay Area Mahout Meetup, Redwood City, August 2013 
+* [New Directions in Mahout’s Recommenders](http://de.slideshare.net/sscdotopen/new-directions-in-mahouts-recommenders) - Sebastian Schelter at Recommender Systems Get Together Berlin, April 2013
+* [Introduction to Mahout and Machine Learning](http://www.slideshare.net/VaradMeru/introduction-to-mahout-and-machine-learning) - Slides by Varad Meru, Software Development Engineer at Orzota. July 27th, 2013.
+* [An Introduction to Collaborative Filtering with Apache Mahout](http://de.slideshare.net/sscdotopen/introduction-to-collaborative-filtering-with-apache-mahout) - Sebastian Schelter at Recommender Systems Challenge Workshop in conjunction with ACM RecSys 2012, Dublin, September 2012
+* [How to build a recommender system based on Mahout and JavaEE](https://github.com/ManuelB/facebook-recommender-demo/raw/master/docs/Talk-BedCon-Berlin-2012.pdf) - Slides by Manuel Blechschmidt at Berlin Expert Days March, 2012.
+* [Apache Mahout for intelligent data analysis](http://lanyrd.com/2011/apachecon-north-america/skdtb/) - Slides from Isabel Drost at Apache Con NA November, 2011.
+* [Dr. Mahout: Analyzing clinical data using scalable and distributed computing](http://lanyrd.com/2011/apachecon-north-america/skdrk/) - Slides from Shannon Quinn at Apache Con NA November, 2011.
+* Frank Scholten at Berlin Buzzwords on June 7, 2011.
+* Introduction to Collaborative Filtering using Mahout (updated) - Talk by Sean Owen at the London Hadoop User Group on April 14, 2011.
+*  [Cool Tricks with Classifiers](http://www.meetup.com/LA-HUG/pages/Video_from_March_16th_LA-HUG_Ted_Dunning_Mahout) - Talk by Ted Dunning at the Los Angeles HUG talking about Mahout classifiers on March 16, 2011.
+* First Mahout Hackathon, Berlin, March 2011
+* [Mahout meetup](http://blog.jteam.nl/2011/01/13/announcement-lucene-nl-mahout-meetup-with-isabel-drost-feb-7/) - there were two talks at the Apache Mahout meetup at JTeam in Amsterdam, February 2011. [intro slides](http://isabel-drost.de/hadoop/slides/jteam.pdf)
+* [Mahout clustering](http://www.fosdem.org/2011/schedule/event/mahoutclustering.html) - Talk on Mahout clustering at data dev room FOSDEM, February 2011.
+* Scaling Data Analysis with Apache Mahout - talk on Mahout at O'Reilly Strata, February 2011. 
+* [Practical Machine Learning](http://www.slideshare.net/jaganadhg/mahout-tutorial-fossmeet-nitc) - Slides from Biju B and Jaganadh G, FOSSMEET-NITC, Calicut, India, February 2011.
+* [Mahout at AlphaCSPs The Edge 2010 (pdf)](http://www.javaedge.com/jedge/pdf/Mahout.pdf) - [slideshare](http://www.slideshare.net/arikogan/mahouts-presentation-at-alphacsps-the-edge-2010) - Slides from [Ariel Kogan](http://il.linkedin.com/in/arielkogan) AlphaCSP's The Edge, December 2010.
+* [Intelligent data analysis with Apache Mahout](http://isabel-drost.de/hadoop/slides/devoxx.pdf) - Slides from Isabel Drost, Devoxx Antwerp, November 2010.
+* [Apache Mahout introduction](http://isabel-drost.de/hadoop/slides/codebits.pdf) - Slides from Isabel Drost, codebits Lisbon, November 2010.
+* [Apache Mahout - Making Data Analysis Easy](http://isabel-drost.de/hadoop/slides/apachecon_2010.pdf) - Slides from Isabel Drost, Apache Con US Atlanta, November 2010.
+* [Practical Machine Learning](http://www.slideshare.net/jaganadhg/bck9) - Slides from Jaganadh G, BarCamp Kerala 9, November 2010.
+* [Mahout and its new classification framework](http://www.slideshare.net/tdunning/sdforum-11042010) - Slides from Ted Dunning, SDForum, November 2010.
+* [Distributed Item-based Collaborative Filtering with Apache Mahout](http://www.slideshare.net/sscdotopen/mahoutcf) - Slides from Sebastian Schelter, Hadoop Get Together Berlin, October 2010.
+* [Hidden Markov Models for Mahout](http://isabel-drost.de/hadoop/slides/HMM.pdf) - Slides from Max Heimel, Hadoop Get Together Berlin, October 2010.
+* [Apache Mahout Mammoth Scale Machine Learning](http://www.slideshare.net/robinanil/oscon-apache-mahout-mammoth-scale-machine-learning) - Slides from Robin Anil, OSCON 2010.
+* [Intro to Apache Mahout](http://slidesha.re/9LxOIu) - Slides from Grant Ingersoll,  RTP Semantic Web Group.
+* [Case study: Biometric Databases and Hadoop](http://www.slideshare.net/ydn/3-biometric-hadoopsummit2010) - Slides from Jason Trost, Hadoop Summit 2010.
+* [Spam Fighting at Yahoo](http://www.slideshare.net/hadoopusergroup/mail-antispam?from=ss_embed)
+* [Web Mining with Ken Krugler](http://www.slideshare.net/hadoopusergroup/bixo-hug-talk?from=ss_embed)
+* [Keynote on intelligent search](http://berlinbuzzwords.wikidot.com/local--files/links-to-slides/ingersoll_bbuzz2010.pdf) - Slides from Grant Ingersoll, Berlin Buzzwords, June 2010.
+* [Simple co-occurrence-based recommendation on Hadoop](http://berlinbuzzwords.wikidot.com/local--files/links-to-slides/owen_bbuzz2010.pdf) - Slides from Sean Owen, Berlin Buzzwords, June, 2010.
+* [Introduction to Collaborative Filtering using Mahout](http://berlinbuzzwords.wikidot.com/local--files/links-to-slides/scholten_bbuzz2010.odp) - Slides from Frank Scholten, Berlin Buzzwords, June, 2010.
+* [Introduction to Scalable Machine Learning](http://lucene.grantingersoll.com/2010/02/16/trijug-intro-to-mahout-slides-and-demo-examples/) - Slides and demos from Grant Ingersoll, March, 2010.
+* Mahout @ India Hadoop Summit - Slides from a 1 hour talk on Mahout at the India Hadoop Summit by Robin Anil, February 2010.
+* [Mahout in 10 minutes](http://www.isabel-drost.de/hadoop/slides/opensourceexpo09.pdf) - Slides from a 10 min intro to Mahout at the Map Reduce tutorial by David Zülke at Open Source Expo in Karlsruhe, Isabel Drost, November 2009.
+* [Mahout at Apache Con US](http://www.isabel-drost.de/hadoop/slides/apacheconus2009.pdf) - Slides from a talk on "Going from raw data to information" (with Mahout) at Apache Con US in Oakland, Isabel Drost, November 2009.
+* [Mahout at FrOSCon](http://www.isabel-drost.de/hadoop/slides/froscon2009.pdf) - Slides from a talk on Mahout at FrOSCon in Sankt Augustin, Isabel Drost, August 2009.
+* [Mahout at DAI group TU Berlin](http://www.isabel-drost.de/hadoop/slides/dai.pdf) - Slides from a talk on Mahout at the DAI Laboratories TU Berlin, Isabel Drost, July 2009.
+* [Mahout at Machine Learning Group TU Berlin](http://www.isabel-drost.de/hadoop/slides/ulf.pdf) - Slides from a talk on Hadoop with some detour to Mahout at the Machine Learning Group of Prof. Dr. Klaus-Robert Müller at TU Berlin, Isabel Drost, June 2009.
+* [Mahout at Google Zürich](http://www.isabel-drost.de/hadoop/slides/google.pdf) - Slides from a Google tech-talk on the past, present and future of Mahout, Isabel Drost, May 2009.
+* [Hadoop user group UK](http://static.last.fm/johan/huguk-20090414/isabel_drost-introducing_apache_mahout.pdf) - Slides from a talk on April 14, 2009 at the Hadoop User Group UK in London, Isabel Drost, April 2009.
+* [BI Over Petabytes: Meet Apache Mahout](http://cwiki.apache.org/confluence/download/attachments/88410/SDForum.pdf) - Slides from a talk by Jeff Eastman on April 21, 2009 at the Bay Area SD Forum Business Intelligence SIG meeting at SAP in Palo Alto, CA.
+* Lucene Meetup and Apache Barcamp in Amsterdam, March 2009.
+* BarCampRDU - (Raleigh) on Aug. 2, 2008
+* Introducing Mahout: Apache Machine Learning - Committer Grant Ingersoll gave a gentle introduction to Mahout and Machine Learning at ApacheCon in November (3rd through 7th) in New Orleans, USA. 
+* Mahout: Scaling Machine Learning - Introduction to Mahout and machine learning at FrOSCon in Sankt Augustin/Germany, Isabel Drost, August 2008.  ([slides](http://cwiki.apache.org/confluence/download/attachments/88410/froscon.pdf))
+* Mahout: Scalable Machine Learning - An introduction to Mahout and machine learning at the first German Hadoop gathering in newthinking store/ Berlin, Isabel Drost, July 2008.
+* Apache Mahout: Industrial Strength Machine Learning - Committer Jeff Eastman gave an introduction to Mahout at Yahoo!, May 2008
+* [Apache Lucene - Mach's wie Google](http://people.apache.org/~berndf/openexpode08-lucene-talk.pdf) - Bernd Fondermann presented an overview of the Apache Lucene project, including Mahout at Open Source Expo 2008 in Karlsruhe, May 2008.
+* Apache Mahout: Bringing Machine Learning to Industrial Strength - Committer Isabel Drost gave a Fast Feather introduction the the new project Mahout at Apache Con EU April, 2008
diff --git a/docs/learning/professional-support.md b/docs/learning/professional-support.md
new file mode 100644
index 000000000..6e5d3e9dd
--- /dev/null
+++ b/docs/learning/professional-support.md
@@ -0,0 +1,55 @@
+---
+title: Professional Support
+sidebar_label: Professional Support
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Professional Support for Mahout
+
+Add yourself or your company if you offer support, consulting, or training for Mahout users, whether for Qumat and QDP or for Mahout Classic. To be listed, open a pull request against this page or email dev@mahout.apache.org. Please keep the lists in alphabetical order.
+
+An entry here is not an endorsement by the Apache Software Foundation nor any of its committers.
+
+## People and companies for hire
+
+| Name | Contact details | Notes |
+|------|-----------------|-------|
+| Aboriginal Armadillo, LLC | trevor@aboriginal-armadillo.com | [Commercial support](http://www.aboriginal-armadillo.com/) |
+| Boston Predictive Analytics | sray@bostonpredictiveanalytics.com | [tutorteddy.com](http://tutorteddy.com/site/free_statistics_help.php) |
+| Frank Scholten | frank.scholten@orange11.nl | |
+| GridLine | [gridline.nl/contact](http://www.gridline.nl/contact) | Specialised in search and thesauri |
+| Jagdish Nomula | nomulaj@gmail.com | ML, search, algorithms, Java. [kosmex.com](http://www.kosmex.com) |
+| Jie-Kai Chang | jiekaichang@apache.org | Apache Mahout PMC Chair and co-creator of QDP. GPU-accelerated quantum data encoding (CUDA/ROCm), zero-copy data pipelines, and Kubernetes-native ML infrastructure (Ray, KubeRay). [github.com/400Ping](https://github.com/400Ping) |
+| LucidWorks | [lucidworks.com](http://www.lucidworks.com) | Big data platform including Mahout as a service for clustering, classification and more |
+| Sematext International | [sematext.com](http://sematext.com/) | |
+| Ted Dunning | sales@maprtech.com | Full commercial support |
+| Winterwell | daniel@winterwell.com | Business and maths concept development, algorithms. [winterwell.com](http://winterwell.com) |
+
+## Talks and presentations
+
+People available to give talks about Mahout at meetups, conferences, and companies.
+
+| Name | Contact details | Notes |
+|------|-----------------|-------|
+| Andrew Musselman | akm@apache.org | ["Building a Recommender with Apache Mahout on Amazon Elastic-MapReduce"](https://blogs.aws.amazon.com/bigdata/post/Tx1TDK3HHBD4EZL/Building-a-Recommender-with-Apache-Mahout-on-Amazon-Elastic-MapReduce-EMR) |
+| Frank Scholten | frank@jteam.nl | Mahout/Taste. [blog.jteam.nl/author/frank](http://blog.jteam.nl/author/frank/) |
+| Isabel Drost-Fromm | isabel@apache.org | If travel and accommodation costs are covered, scheduling a talk is a lot easier. |
+| Jie-Kai Chang | jiekaichang@apache.org | Qumat and QDP, GPU-accelerated quantum data encoding. See [Community Over Code Asia 2026](https://asia.communityovercode.org/sessions/general-1194670.html). |
+
+The entries above date from the Mahout Classic era unless noted. Contact details were supplied by the people listed and may no longer be current.
diff --git a/docs/learning/reference-reading.md b/docs/learning/reference-reading.md
new file mode 100644
index 000000000..583466aeb
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+++ b/docs/learning/reference-reading.md
@@ -0,0 +1,79 @@
+---
+title: Reference Reading
+sidebar_label: Reference Reading
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Reference Reading
+
+Here we provide references to books and courses about data analysis in general, which might also be helpful in the context of Mahout.
+
+### General Background Materials
+
+Don't be overwhelmed by all the maths, you can do a lot in Mahout with some
+basic knowledge. The books will help you understand your
+data better, and ask better questions both of Mahout's APIs, and also of
+the Mahout community. And unlike learning some particular software tool,
+these are skills that will remain useful decades later.
+* [Gilbert Strang](http://www-math.mit.edu/~gs)
+'s [Introduction to Linear Algebra](http://math.mit.edu/linearalgebra/). His [lectures](http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) are also [available online](http://web.mit.edu/18.06/www/)
+ and are strongly recommended. 
+* [Mathematical Tools for Applied Mulitvariate Analysis](http://www.amazon.com/Mathematical-Tools-Applied-Multivariate-Analysis/dp/0121609553/ref=sr_1_1?ie=UTF8&qid=1299602805&sr=8-1) by J.Douglass
+Carroll.
+* [Stanford Machine Learning online courseware](http://www.stanford.edu/class/cs229/)
+* [MIT Machine Learning online courseware](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/)  has [lecture notes](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/) online.
+* As a pre-requisite to probability and statistics, you'll need [basic calculus](http://en.wikipedia.org/wiki/Calculus). A maths for scientists text might be useful here such as 'Mathematics for Engineers and Scientists', Alan Jeffrey, Chapman & Hall/CRC. ([openlibrary](http://openlibrary.org/books/OL3305993M/Mathematics_for_engineers_and_scientists))
+* One of the best writers in the probability/statistics world is Sheldon Ross. Try [A First Course in Probability (8th Edition)](http://www.pearsonhighered.com/educator/product/First-Course-in-Probability-A/9780136033134.page) and then move on to his [Introduction to Probability Models](http://www.amazon.com/Introduction-Probability-Models-Sixth-Sheldon/dp/0125984707)
+
+Some good introductory alternatives here are:
+* [Kahn Academy](http://www.khanacademy.org/) -- videos on stats, probability, linear algebra
+* [Probability and Statistics (7th Edition)](http://www.amazon.com/Probability-Statistics-Engineering-Sciences-InfoTrac/dp/0534399339), Jay L. Devore, Chapman.
+* [Probability and Statistical Inference (7th Edition)](http://www.amazon.com/Probability-Statistical-Inference-Robert-Hogg/dp/0132546086), Hogg and Tanis, Pearson.
+
+Once you have a grasp of the basics then there are a slew of great texts that you might consult:
+* [Statistical Inference](http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126), Casell and Berger, Duxbury/Thomson Learning.
+* [Introduction to Bayesian Statistics](http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202), William H. Bolstad, Wiley. 
+* [Understanding Computational Bayesian Statistics](http://www.amazon.com/Understanding-Computational-Bayesian-Statistics-Wiley/dp/0470046090), Bolstadt
+* [Bayesian Data Analysis, Gelman et al.](http://www.stat.columbia.edu/~gelman/book/)
+
+### For statistics related to machine learning, these are particularly helpful:
+
+* [Pattern Recognition and Machine Learning by Chris Bishop](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
+* [Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) by Trevor Hastie, Robert Tibshirani, Jerome Friedman 
+* [http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
+ 
+
+### For matrix computations/decomposition/factorization etc.:
+
+* Peter V. O'Neil [Introduction to Linear Algebra](http://www.amazon.com/Introduction-Linear-Algebra-Theory-Applications/dp/053400606X), great book for beginners (with some knowledge in calculus). It is not comprehensive, but, it will be a good place to start and the author starts by explaining the concepts with regards to vector spaces which I found to be a more natural way of explaining.
+* David S. Watkins [Fundamentals of Matrix Computations](http://www.amazon.com/Fundamentals-Matrix-Computations-Applied-Mathematics/dp/0470528338/)
+* [Matrix Computations](http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan) is the classic text for numerical linear algebra. Can't go wrong with it - great for researchers.  
+* Nick Trefethen's [Numerical Linear Algebra](http://people.maths.ox.ac.uk/trefethen/books.html).  It's a bit more approachable for practitioners. Many chapters on SVD, there are even chapters on Lanczos.
+
+### Books specifically on R:
+
+* Learning about R is a difficult thing. The best introduction is in MASS [http://www.stats.ox.ac.uk/pub/MASS4/](http://www.stats.ox.ac.uk/pub/MASS4/)
+* [R Tutor](http://www.r-tutor.com/r-introduction)
+* [Manual](http://cran.r-project.org/doc/manuals/R-intro.pdf)
+* [R Course](http://faculty.washington.edu/tlumley/Rcourse/)
+
+In addition, you should see how to plot data well:
+
+* [Trellis plotting](http://www.statmethods.net/advgraphs/trellis.html)
+* [ggplot2](http://had.co.nz/ggplot2/)
diff --git a/website/docusaurus.config.ts b/website/docusaurus.config.ts
index 820c79cda..920668941 100644
--- a/website/docusaurus.config.ts
+++ b/website/docusaurus.config.ts
@@ -237,6 +237,16 @@ const config: Config = {
               docId: 'learning/papers/index',
               label: 'Research Papers',
             },
+            {
+              type: 'doc',
+              docId: 'learning/books-tutorials-and-talks',
+              label: 'Books, Tutorials & Talks',
+            },
+            {
+              type: 'doc',
+              docId: 'learning/professional-support',
+              label: 'Professional Support',
+            },
           ],
         },
         // Community dropdown
diff --git a/website/sidebars.ts b/website/sidebars.ts
index 168d277bb..777772c63 100644
--- a/website/sidebars.ts
+++ b/website/sidebars.ts
@@ -92,6 +92,9 @@ const sidebars: SidebarsConfig = {
             'learning/papers/Unleashing-the-Potential-of-LLMs-for-Quantum-Computing',
           ],
         },
+        'learning/books-tutorials-and-talks',
+        'learning/reference-reading',
+        'learning/professional-support',
       ],
     },
     {
diff --git a/website/src/data/powered-by.json b/website/src/data/powered-by.json
index 620991e75..341f0ff80 100644
--- a/website/src/data/powered-by.json
+++ b/website/src/data/powered-by.json
@@ -2,6 +2,7 @@
   {
     "name": "Adobe",
     "url": "https://www.adobe.com",
+    "type": "commercial",
     "category": "User targeting",
     "description": "Adobe AMP used Mahout's clustering algorithms to increase video consumption through better user targeting.",
     "source": "http://nosql.mypopescu.com/post/2082712431/hbase-and-hadoop-at-adobe",
@@ -10,6 +11,7 @@
   {
     "name": "AOL",
     "url": "https://www.aol.com",
+    "type": "commercial",
     "category": "Recommendations",
     "description": "AOL used Mahout for shopping recommendations.",
     "source": "https://www.slideshare.net/kryton/the-data-layer",
@@ -18,6 +20,7 @@
   {
     "name": "Booz Allen Hamilton",
     "url": "https://www.boozallen.com",
+    "type": "commercial",
     "category": "Clustering",
     "description": "Booz Allen Hamilton used Mahout's clustering algorithms for large-scale biometric analysis.",
     "source": "https://www.slideshare.net/ydn/3-biometric-hadoopsummit2010",
@@ -26,6 +29,7 @@
   {
     "name": "Drupal",
     "url": "https://www.drupal.org/project/recommender",
+    "type": "commercial",
     "category": "Content recommendation",
     "description": "The Drupal Recommender API module used Mahout to provide open source content recommendation for Drupal sites.",
     "logo": "/img/powered-by/drupal.svg"
@@ -33,6 +37,7 @@
   {
     "name": "Foursquare",
     "url": "https://foursquare.com",
+    "type": "commercial",
     "category": "Recommendations",
     "description": "Foursquare used Mahout for its venue recommendation engine.",
     "source": "https://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/",
@@ -41,6 +46,7 @@
   {
     "name": "Intel",
     "url": "https://www.intel.com",
+    "type": "commercial",
     "category": "Distribution",
     "description": "Intel shipped Mahout as part of its Distribution for Apache Hadoop Software.",
     "logo": "/img/powered-by/intel.svg"
@@ -48,6 +54,7 @@
   {
     "name": "LinkedIn",
     "url": "https://www.linkedin.com",
+    "type": "commercial",
     "category": "Model training",
     "description": "LinkedIn experimented with Mahout for model training as an alternative to R-based workflows.",
     "logo": "/img/powered-by/linkedin.svg"
@@ -55,6 +62,7 @@
   {
     "name": "LucidWorks",
     "url": "https://www.lucidworks.com",
+    "type": "commercial",
     "category": "Search and text analytics",
     "description": "LucidWorks Big Data used Mahout for clustering, duplicate document detection, phrase extraction, and classification.",
     "logo": "/img/powered-by/lucidworks.png"
@@ -62,6 +70,7 @@
   {
     "name": "Mendeley",
     "url": "https://www.mendeley.com",
+    "type": "commercial",
     "category": "Recommendations",
     "description": "Mendeley used Mahout to power Mendeley Suggest, a research article recommendation service.",
     "logo": "/img/powered-by/mendeley.png"
@@ -69,6 +78,7 @@
   {
     "name": "NewsCred",
     "url": "https://www.newscred.com",
+    "type": "commercial",
     "category": "News clustering",
     "description": "NewsCred used Mahout to cluster news articles and surface the important stories of the day.",
     "logo": "/img/powered-by/newscred.png"
@@ -76,6 +86,7 @@
   {
     "name": "ResearchGate",
     "url": "https://www.researchgate.net",
+    "type": "commercial",
     "category": "Recommendations",
     "description": "ResearchGate, the professional network for scientists and researchers, used Mahout's recommendation algorithms.",
     "logo": "/img/powered-by/researchgate.svg"
@@ -83,6 +94,7 @@
   {
     "name": "Sematext",
     "url": "https://sematext.com",
+    "type": "commercial",
     "category": "Recommendations",
     "description": "Sematext used Mahout for its recommendation engine.",
     "logo": "/img/powered-by/sematext.png"
@@ -90,13 +102,15 @@
   {
     "name": "Twitter",
     "url": "https://twitter.com",
+    "type": "commercial",
     "category": "Topic modeling",
-    "description": "Twitter used Mahout's LDA implementation for user interest modeling.",
+    "description": "Twitter used Mahout's LDA implementation for user interest modeling, and maintained a fork of Mahout on GitHub that was periodically synced with Apache trunk.",
     "logo": "/img/powered-by/twitter.svg"
   },
   {
     "name": "Yahoo! Mail",
     "url": "https://mail.yahoo.com",
+    "type": "commercial",
     "category": "Anti-spam",
     "description": "Yahoo! Mail used Mahout's frequent pattern set mining in its anti-spam pipeline.",
     "source": "https://www.slideshare.net/hadoopusergroup/mail-antispam",
@@ -105,6 +119,7 @@
   {
     "name": "TU Berlin",
     "url": "https://www.dima.tu-berlin.de",
+    "type": "academic",
     "category": "Teaching",
     "description": "The Large Scale Data Analysis and Data Mining course at TU Berlin used Mahout to teach students how to parallelize data mining problems.",
     "logo": "/img/powered-by/tu-berlin.svg"
@@ -112,6 +127,7 @@
   {
     "name": "Carnegie Mellon University",
     "url": "https://www.cmu.edu",
+    "type": "academic",
     "category": "Research",
     "description": "Carnegie Mellon University used Mahout as a comparison platform in GraphLab research.",
     "logo": "/img/powered-by/carnegie-mellon-university.svg"
@@ -119,6 +135,7 @@
   {
     "name": "Nagoya Institute of Technology",
     "url": "https://www.nitech.ac.jp",
+    "type": "academic",
     "category": "Research",
     "description": "Nagoya Institute of Technology used Mahout for research and data processing in a large-scale citizen participation platform funded by the Ministry of Interior of Japan.",
     "logo": "/img/powered-by/nagoya-institute-of-technology.png"
@@ -126,6 +143,7 @@
   {
     "name": "DERI, NUI Galway",
     "url": "https://www.universityofgalway.ie",
+    "type": "academic",
     "category": "Research",
     "description": "Researchers at the Digital Enterprise Research Institute used Mahout for topic mining and modelling of large corpora.",
     "logo": "/img/powered-by/deri-nui-galway.png"
@@ -133,75 +151,167 @@
   {
     "name": "Accenture",
     "url": "https://www.accenture.com",
+    "type": "commercial",
+    "category": "Consulting",
+    "description": "Accenture used Mahout as the typical example in its Hadoop Deployment Comparison Study.",
+    "source": "https://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Hadoop-Deployment-Comparison-Study.pdf",
     "logo": "/img/powered-by/accenture.svg"
   },
   {
     "name": "Buzzlogic",
-    "url": "https://www.buzzlogic.com"
+    "url": "https://www.buzzlogic.com",
+    "type": "commercial",
+    "category": "Ad targeting",
+    "description": "Buzzlogic used Mahout's clustering algorithms to improve ad targeting."
   },
   {
     "name": "DataMine Lab",
     "url": "https://dataminelab.com",
+    "type": "commercial",
+    "category": "Ad targeting",
+    "description": "DataMine Lab used Mahout's recommendation and clustering algorithms to improve its clients' ad targeting.",
     "logo": "/img/powered-by/datamine-lab.jpg"
   },
   {
     "name": "Idealo",
     "url": "https://www.idealo.de",
+    "type": "commercial",
+    "category": "Recommendations",
+    "description": "Idealo used Mahout's recommendation engine.",
     "logo": "/img/powered-by/idealo.svg"
   },
   {
     "name": "InfoGlutton",
-    "url": "https://www.infoglutton.com"
+    "url": "https://www.infoglutton.com",
+    "type": "commercial",
+    "category": "Consulting",
+    "description": "InfoGlutton used Mahout's clustering and classification for various consulting projects."
   },
   {
     "name": "Intela",
-    "url": "https://www.intela.com"
+    "url": "https://www.intela.com",
+    "type": "commercial",
+    "category": "Recommendations",
+    "description": "Intela implemented Mahout's recommendation algorithms to select new offers to send to customers and to recommend potential customers for current offers, and used the clustering algorithms to enhance its offer categories."
   },
   {
     "name": "iOffer",
     "url": "https://www.ioffer.com",
+    "type": "commercial",
+    "category": "Recommendations",
+    "description": "iOffer used Mahout's frequent pattern mining and collaborative filtering to recommend items to users.",
     "logo": "/img/powered-by/ioffer.gif"
   },
   {
     "name": "Kauli",
     "url": "https://kau.li",
+    "type": "commercial",
+    "category": "Ad targeting",
+    "description": "Kauli, a Japanese ad network, used Mahout's clustering to handle clickstream data for predicting audience interests and intents.",
     "logo": "/img/powered-by/kauli.gif"
   },
   {
     "name": "Mobage",
     "url": "https://mbga.jp",
+    "type": "commercial",
+    "category": "Analytics",
+    "description": "Mobage used Mahout in its analysis pipeline.",
+    "source": "https://www.slideshare.net/hamadakoichi/mobage-prmu-2011-mahout-hadoop",
     "logo": "/img/powered-by/mobage.png"
   },
   {
     "name": "Myrrix",
     "url": "https://myrrix.com",
+    "type": "commercial",
+    "category": "Recommender product",
+    "description": "Myrrix was a recommender system product built on Mahout.",
     "logo": "/img/powered-by/myrrix.png"
   },
   {
     "name": "Predixion Software",
     "url": "https://predixionsoftware.com",
+    "type": "commercial",
+    "category": "Predictive modeling",
+    "description": "Predixion Software used Mahout's algorithms to build predictive models on big data.",
     "logo": "/img/powered-by/predixion-software.png"
   },
   {
     "name": "Radoop",
-    "url": "https://radoop.eu"
+    "url": "https://radoop.eu",
+    "type": "commercial",
+    "category": "Analytics platform",
+    "description": "Radoop provided a drag-and-drop interface for big data analytics, including Mahout clustering and classification algorithms."
   },
   {
     "name": "SpeedDate",
-    "url": "https://www.speeddate.com"
+    "url": "https://www.speeddate.com",
+    "type": "commercial",
+    "category": "Recommendations",
+    "description": "SpeedDate.com used Mahout's collaborative filtering engine to recommend member profiles."
   },
   {
     "name": "365Media",
     "url": "https://365media.com",
+    "type": "commercial",
+    "category": "Classification",
+    "description": "365Media used Mahout's classification and collaborative filtering algorithms in its real-time system UPTIME and in 365Media/Social.",
     "logo": "/img/powered-by/365media.jpg"
   },
   {
     "name": "Dicode project",
     "url": "https://www.dicode-project.eu",
+    "type": "academic",
+    "category": "Research",
+    "description": "The Dicode project used Mahout's clustering and classification algorithms on top of HBase.",
     "logo": "/img/powered-by/dicode-project.png"
   },
   {
     "name": "ROBUST project",
-    "url": "https://www.robust-project.eu"
+    "url": "https://www.robust-project.eu",
+    "type": "academic",
+    "category": "Research",
+    "description": "The ROBUST project, co-funded by the European Commission, employed Mahout in the large-scale analysis of online community data."
+  },
+  {
+    "name": "Amazon",
+    "url": "https://www.amazon.com",
+    "type": "commercial",
+    "category": "Personalization",
+    "description": "Amazon's Personalization Platform used Mahout."
+  },
+  {
+    "name": "Cull.tv",
+    "url": "http://cull.tv/",
+    "type": "commercial",
+    "category": "Content recommendation",
+    "description": "Cull.tv used modified Mahout algorithms for content recommendations."
+  },
+  {
+    "name": "Evolv",
+    "url": "http://www.evolvondemand.com",
+    "type": "commercial",
+    "category": "Workforce analytics",
+    "description": "Evolv used Mahout for its Workforce Predictive Analytics platform."
+  },
+  {
+    "name": "Mippin",
+    "url": "http://mippin.com",
+    "type": "commercial",
+    "category": "Recommendations",
+    "description": "Mippin used Mahout's collaborative filtering engine to recommend news feeds."
+  },
+  {
+    "name": "Next Glass",
+    "url": "http://nextglass.co/",
+    "type": "commercial",
+    "category": "Recommendations",
+    "description": "Next Glass used Mahout."
+  },
+  {
+    "name": "NoTube project",
+    "url": "http://notube.tv/",
+    "type": "academic",
+    "category": "Research",
+    "description": "The NoTube EU project used Mahout's Taste recommender in its applications."
   }
 ]
diff --git a/website/src/pages/powered-by.module.css b/website/src/pages/powered-by.module.css
index 414784389..d67efe945 100644
--- a/website/src/pages/powered-by.module.css
+++ b/website/src/pages/powered-by.module.css
@@ -33,7 +33,7 @@
 
 .cards {
   display: grid;
-  grid-template-columns: repeat(auto-fill, minmax(320px, 1fr));
+  grid-template-columns: repeat(auto-fill, minmax(300px, 1fr));
   gap: 1.25rem;
 }
 
@@ -52,16 +52,48 @@
   box-shadow: 0 4px 16px rgba(0, 0, 0, 0.06);
 }
 
-.cardHeader {
+.logoBox {
   display: flex;
-  justify-content: space-between;
-  align-items: flex-start;
-  gap: 1rem;
-  margin-bottom: 0.75rem;
+  align-items: center;
+  justify-content: center;
+  height: 96px;
+  padding: 0.75rem 1rem;
+  margin-bottom: 1rem;
+  border-radius: 8px;
+  background: #fff;
+  text-decoration: none;
+}
+
+[data-theme='dark'] .logoBox {
+  border: 1px solid var(--ifm-color-emphasis-200);
+}
+
+.logoBox:hover {
+  text-decoration: none;
+}
+
+.cardLogo {
+  height: 56px;
+  width: auto;
+  max-width: 85%;
+  object-fit: contain;
+}
+
+.logoFallback {
+  font-family: var(--ifm-heading-font-family);
+  font-size: 1.35rem;
+  font-weight: 700;
+  color: #1c1e21;
+  text-align: center;
+  line-height: 1.2;
+}
+
+.cardHeader {
+  margin-bottom: 0.5rem;
 }
 
 .cardName {
-  font-size: 1.25rem;
+  font-size: 1.15rem;
   font-weight: 700;
   margin: 0;
 }
@@ -89,28 +121,6 @@
   color: var(--ifm-color-primary);
 }
 
-.cardLogo {
-  height: 44px;
-  width: auto;
-  max-width: 160px;
-  object-fit: contain;
-  flex-shrink: 0;
-}
-
-.logoTile {
-  display: inline-flex;
-  align-items: center;
-  justify-content: center;
-  padding: 0.5rem 0.75rem;
-  border-radius: 8px;
-  background: #fff;
-  flex-shrink: 0;
-}
-
-[data-theme='dark'] .logoTile {
-  border: 1px solid var(--ifm-color-emphasis-200);
-}
-
 .cardBody {
   flex: 1;
   margin: 0;
@@ -122,53 +132,6 @@
   font-size: 0.9rem;
 }
 
-.wall {
-  display: grid;
-  grid-template-columns: repeat(auto-fill, minmax(180px, 1fr));
-  gap: 1rem;
-}
-
-.wallItem {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  justify-content: center;
-  gap: 0.6rem;
-  min-height: 120px;
-  padding: 1rem;
-  border: 1px solid var(--ifm-color-emphasis-200);
-  border-radius: 12px;
-  background: #fff;
-  color: var(--ifm-color-emphasis-800);
-  font-weight: 600;
-  font-size: 0.9rem;
-  text-align: center;
-  text-decoration: none;
-  transition: border-color 0.15s ease, box-shadow 0.15s ease;
-}
-
-[data-theme='dark'] .wallItem {
-  color: #1c1e21;
-}
-
-.wallItem:hover {
-  border-color: var(--ifm-color-primary);
-  box-shadow: 0 4px 16px rgba(0, 0, 0, 0.08);
-  text-decoration: none;
-  color: var(--ifm-color-primary-darker);
-}
-
-.wallLogo {
-  height: 64px;
-  width: auto;
-  max-width: 160px;
-  object-fit: contain;
-}
-
-.wallName {
-  font-size: 1.1rem;
-}
-
 .cta {
   padding: 3rem 0 4rem;
 }
diff --git a/website/src/pages/powered-by.tsx b/website/src/pages/powered-by.tsx
index 56a392199..8afd04daf 100644
--- a/website/src/pages/powered-by.tsx
+++ b/website/src/pages/powered-by.tsx
@@ -10,6 +10,7 @@ import styles from './powered-by.module.css';
 type Entry = {
   name: string;
   url: string;
+  type: 'commercial' | 'academic';
   category?: string;
   description?: string;
   source?: string;
@@ -22,10 +23,12 @@ const DATA_FILE_URL =
   'https://github.com/apache/mahout/blob/main/website/src/data/powered-by.json';
 
 const allEntries = entries as Entry[];
-const featured = allEntries.filter((entry) => entry.description);
-const everyone = [...allEntries].sort((a, b) =>
-  a.name.localeCompare(b.name, 'en', {sensitivity: 'base'}),
-);
+const byName = (a: Entry, b: Entry) =>
+  a.name.localeCompare(b.name, 'en', {sensitivity: 'base'});
+const commercial = allEntries
+  .filter((e) => e.type === 'commercial')
+  .sort(byName);
+const academic = allEntries.filter((e) => e.type === 'academic').sort(byName);
 
 function Logo({entry, className}: {entry: Entry; className: string}) {
   const src = useBaseUrl(entry.logo ?? '');
@@ -35,25 +38,27 @@ function Logo({entry, className}: {entry: Entry; className: string}) {
   return <img className={className} src={src} alt={`${entry.name} logo`} />;
 }
 
-function FeaturedCard({entry}: {entry: Entry}) {
+function Card({entry}: {entry: Entry}) {
   return (
     <article className={styles.card}>
+      <Link className={styles.logoBox} href={entry.url} title={entry.name}>
+        {entry.logo ? (
+          <Logo entry={entry} className={styles.cardLogo} />
+        ) : (
+          <span className={styles.logoFallback}>{entry.name}</span>
+        )}
+      </Link>
       <div className={styles.cardHeader}>
-        <div>
-          <h3 className={styles.cardName}>
-            <Link href={entry.url}>{entry.name}</Link>
-          </h3>
-          {entry.category && (
-            <span className={styles.cardCategory}>{entry.category}</span>
-          )}
-        </div>
-        {entry.logo && (
-          <span className={styles.logoTile}>
-            <Logo entry={entry} className={styles.cardLogo} />
-          </span>
+        <h3 className={styles.cardName}>
+          <Link href={entry.url}>{entry.name}</Link>
+        </h3>
+        {entry.category && (
+          <span className={styles.cardCategory}>{entry.category}</span>
         )}
       </div>
-      <p className={styles.cardBody}>{entry.description}</p>
+      {entry.description && (
+        <p className={styles.cardBody}>{entry.description}</p>
+      )}
       {entry.source && (
         <p className={styles.cardSource}>
           <Link href={entry.source}>Source</Link>
@@ -63,15 +68,29 @@ function FeaturedCard({entry}: {entry: Entry}) {
   );
 }
 
-function WallItem({entry}: {entry: Entry}) {
+function UseSection({
+  title,
+  subtitle,
+  items,
+}: {
+  title: string;
+  subtitle: string;
+  items: Entry[];
+}) {
   return (
-    <Link className={styles.wallItem} href={entry.url} title={entry.name}>
-      {entry.logo ? (
-        <Logo entry={entry} className={styles.wallLogo} />
-      ) : (
-        <span className={styles.wallName}>{entry.name}</span>
-      )}
-    </Link>
+    <section className={styles.section}>
+      <div className="container">
+        <Heading as="h2" className={styles.sectionTitle}>
+          {title}
+        </Heading>
+        <p className={styles.sectionSubtitle}>{subtitle}</p>
+        <div className={styles.cards}>
+          {items.map((entry) => (
+            <Card key={entry.name} entry={entry} />
+          ))}
+        </div>
+      </div>
+    </section>
   );
 }
 
@@ -95,8 +114,9 @@ export default function PoweredBy(): ReactNode {
               quantum states.
             </p>
             <p className={styles.lead}>
-              See how organizations, developers, and researchers have built with
-              Mahout, and add your own story.
+              See how companies, developers, and researchers have built with
+              Mahout, and add your own story. Entries are listed alphabetically
+              within each section.
             </p>
             <Link className="button button--primary button--lg" href={SUBMIT_URL}>
               Add your organization
@@ -106,40 +126,16 @@ export default function PoweredBy(): ReactNode {
       </header>
 
       <main>
-        {featured.length > 0 && (
-          <section className={styles.section}>
-            <div className="container">
-              <Heading as="h2" className={styles.sectionTitle}>
-                How Mahout is being used
-              </Heading>
-              <p className={styles.sectionSubtitle}>
-                Use cases the community has shared.
-              </p>
-              <div className={styles.cards}>
-                {featured.map((entry) => (
-                  <FeaturedCard key={entry.name} entry={entry} />
-                ))}
-              </div>
-            </div>
-          </section>
-        )}
-
-        <section className={styles.section}>
-          <div className="container">
-            <Heading as="h2" className={styles.sectionTitle}>
-              Organizations building with Mahout
-            </Heading>
-            <p className={styles.sectionSubtitle}>
-              Companies, projects, and research groups that have told the
-              community they use or have used Apache Mahout.
-            </p>
-            <div className={styles.wall}>
-              {everyone.map((entry) => (
-                <WallItem key={entry.name} entry={entry} />
-              ))}
-            </div>
-          </div>
-        </section>
+        <UseSection
+          title="Commercial Use"
+          subtitle="Companies and products that have built on Apache Mahout."
+          items={commercial}
+        />
+        <UseSection
+          title="Academic Use"
+          subtitle="Universities, research institutes, and funded research projects using Mahout."
+          items={academic}
+        />
 
         <section className={styles.cta}>
           <div className="container">
diff --git a/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md b/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md
new file mode 100644
index 000000000..78ae7d6b4
--- /dev/null
+++ b/website/versioned_docs/version-0.6/learning/books-tutorials-and-talks.md
@@ -0,0 +1,156 @@
+---
+title: Books, Tutorials and Talks
+sidebar_label: Books, Tutorials & Talks
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Books, Tutorials and Talks
+
+This page collects talks, tutorials, articles, books, slides, and lectures about Apache Mahout, from the current Qumat and QDP work back to the Hadoop-era releases. No endorsements are implied or given. To add an entry, open a pull request against this page or email dev@mahout.apache.org.
+
+For general background on linear algebra, statistics, and machine learning, see [Reference Reading](./reference-reading).
+
+## Books
+
+### Qumat and quantum computing
+
+* [Quantum Computing Primer](./quantum-computing-primer/) - A ten-chapter introduction to quantum computing using Qumat, maintained in these docs.
+* [Research Papers](./papers/) - Papers and publications related to Qumat and quantum machine learning.
+
+### Mahout specific
+
+* [Apache Mahout: Beyond MapReduce](http://www.weatheringthroughtechdays.com/2016/02/mahout-samsara-book-is-out.html) by Dmitriy Lyubimov and Andrew Palumbo published Feb 2016. Covers new features in Mahout "Samsara" releases (0.10, 0.11+).
+* [Apache Mahout cookbook](http://www.packtpub.com/apache-mahout-cookbook/book)- Book by Piero Giacomelli published Dec 2013 by Packtpub.
+* [Mahout in Action](http://www.manning.com/owen/) - Book by Sean Owen, Robin Anil, Ted Dunning and Ellen Friedman published Oct 2011 by Manning Publications.
+* [Taming Text](http://www.manning.com/ingersoll/) - By Grant Ingersoll and Tom Morton, published by Manning Publications. Will have some Mahout coverage, but by no means as complete as Mahout in Action.
+
+### Engineering oriented machine learning books
+
+* [Collective Intelligence in Action](http://www.amazon.com/Collective-Intelligence-Action-Satnam-Alag/dp/1933988312/ref=pd_bbs_sr_3?ie=UTF8&s=books&qid=1214545249&sr=1-3)
+* [Programming Collective Intelligence](http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325/ref=pd_bbs_sr_1/104-1017533-9408723?ie=UTF8&s=books&qid=1214593516&sr=1-1)
+* [Algorithms of the Intelligent Web](http://www.amazon.com/Algorithms-Intelligent-Web-Haralambos-Marmanis/dp/1933988665/ref=sr_1_1?s=books&ie=UTF8&qid=1298005918&sr=1-1)
+
+### Scientific background
+
+* [Data Mining: Practical Machine Learning Tools and Techniques](http://www.cs.waikato.ac.nz/~ml/weka/book.html)
+* [Introduction to Information Retrieval](http://www-nlp.stanford.edu/IR-book/)
+* [Machine Learning](http://www.amazon.com/Machine-Learning-Mcgraw-Hill-International-Edit/dp/0071154671/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1214593709&sr=8-1)
+* [Pattern Recognition and Machine Learning (Information Science and Statistics)](http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=pd_bbs_sr_2?ie=UTF8&s=books&qid=1214593709&sr=8-2)
+
+## News, Articles and Tutorials
+
+### Qumat and QDP
+
+* [Introducing QuMat v0.6.0](/blog/2026/06/01/Qumat-0.6.0-Release) - AMD ROCm support, six QDP encodings with CUDA/ROCm parity, and real-workload benchmarks, June 2026
+* [QuMat v0.5.0 release](/blog/2026/02/02/Qumat-0.5.0-Release) - First public QDP proof of concept for GPU state preparation, February 2026
+* [QuMat v0.4.0 release](/blog/2025/04/17/Qumat-0.4.0-Release) - April 2025
+* [Getting Started with Qumat](../qumat/getting-started) and [Getting Started with QDP](../qdp/getting-started) - The official quickstarts
+
+### Mahout Classic
+
+* [Mahout 0.10.x: first Mahout release as a programming environment](http://www.weatheringthroughtechdays.com/2015/04/mahout-010x-first-mahout-release-as.html)   
+* [Comparing Document Classification Functions of Lucene and Mahout](http://soleami.com/blog/comparing-document-classification-functions-of-lucene-and-mahout.html)
+* [Apache Mahout: Scalable Machine Learning for Everyone](http://www.ibm.com/developerworks/java/library/j-mahout-scaling/)
+* [How to build a spam filter server with Mahout](http://emmaespina.wordpress.com/2011/04/26/ham-spam-and-elephants-or-how-to-build-a-spam-filter-server-with-mahout/) - Applying classification on a live server - April 2011
+* [Deploying a massively scalable recommender system with Apache Mahout](http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/) - Blogpost of Sebastian Schelter in April 2011
+* [Apache Mahout & the commoditization of machine learning](http://www.redmonk.com/cote/2010/11/04/makeall013/) - Podcast interview with Grant Ingersoll at ApacheCon 2010
+* [Apache Mahout 0.4 mit neuen Algorithmen](http://isabel-drost.de/hadoop/slides/devoxx.pdf) - published after the 0.4 release by heise Open/ Developer, November 2010
+* [Mahout on InfoQ](http://www.infoq.com/news/2009/04/mahout) - Interview with Grant Ingersoll on InfoQ
+* [Mahout in the Cloudera weblog](http://www.cloudera.com/blog/2009/04/21/hadoop-uk-user-group-meeting/) - published after the Hadoop user group UK.
+* [Mahout in the Drools weblog](http://blog.athico.com/2008/08/machine-learning-and-apache-mahout.html) - Michael Neale published an article on Mahout in the drools weblog
+* [Introducing Apache Mahout](https://www.ibm.com/developerworks/java/library/j-mahout/index.html) - Grant Ingersoll - Intro to Apache Mahout focused on clustering, classification and collaborative filtering. Japanese translation available at: [http://www.ibm.com/developerworks/jp/java/library/j-mahout/](http://www.ibm.com/developerworks/jp/java/library/j-mahout/)
+* [Flexible Collaborative Filtering In Java With Mahout Taste](http://philippeadjiman.com/blog/2009/11/11/flexible-collaborative-filtering-in-java-with-mahout-taste/) - Philippe Adjiman - Quick starting guide on how to use the collaborative filtering package of Mahout (called Taste) to quickly and flexibly create, test and compare tailored recommendation engines.
+* [Integrating Mahout with Lucene and Solr](http://www.lucidimagination.com/blog/2010/03/16/integrating-apache-mahout-with-apache-lucene-and-solr-part-i-of-3/) Three part series on ways to integrate Mahout with Lucene and Solr
+* [Mahout Item Recommender Tutorial using Java and Eclipse](https://www.youtube.com/watch?v=yD40rVKUwPI) - YouTube video tutorial by Steve Cook
+
+## Coursework/Lectures
+
+* [http://videolectures.net/mlss05us_chicago/](http://videolectures.net/mlss05us_chicago/)
+* [http://videolectures.net/mlas06_pittsburgh/](http://videolectures.net/mlas06_pittsburgh/)
+* [Stanford Lectures on Machine Learning by Andrew Ng](http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1)
+* [CMU@Qatar Introduction to Mahout lecture](https://docs.google.com/open?id=0ByhGL2_SCeitMDQ3OTczNjItM2ZjYi00ZDg5LWE0MzItZGQxODQ5NzkzYjNj)
+
+## Talks
+
+In reverse chronological order, so that most recent talks are at the top.
+
+### Qumat and QDP
+
+* [Accelerating Quantum Machine Learning: Building a GPU-Accelerated Data Plane in Apache Mahout](https://asia.communityovercode.org/sessions/general-1194670.html) - Jie-Kai Chang and Guan-Hua Wen at Community Over Code Asia 2026, Beijing, August 8, 2026.
+* [Introducing Qumat!](https://mirrors.dotsrc.org/fosdem/2025/k4401/fosdem-2025-5298-introducing-qumat-an-apache-mahout-joint-.av1.webm) - FOSDEM 2025, Brussels, February 2025.
+* [QuMat: Apache Mahout's Quantum Computing Interface](https://www.youtube.com/watch?v=tgFaUL1wYhY) - FOSSY 2024, Portland, August 2024.
+
+### Mahout Classic
+
+* Distributed Machine Learning with Apache Mahout - Suneel Marthi at Apache Big Data North America, Vancouver, Canada, May 11, 2016 and MapR Washington DC Big Data Everywhere, Tysons, VA, June 2 2016
+* [Declarative Machine Learning with the Samsara DSL](http://www.slideshare.net/FlinkForward/sebastian-schelter-distributed-machine-learing-with-the-samsara-dsl) Sebastian Schelter at Flink Forward Conference, Berlin Germany, October 2015.
+* [Bringing Algebraic Semantics to Mahout](http://www.slideshare.net/sscdotopen/bringing-algebraic-semantics-to-mahout) Sebastian Schelter at HPI Infolunch, Potsdam Germany, May 2014
+* Mahout Spark and Scala bindings: Bringing Algebraic Semantics ([slides](http://www.slideshare.net/DmitriyLyubimov/mahout-scala-and-spark-bindings)/[video](http://youtu.be/h9dpmvNW1Dw)) - Dmitriy Lyubimov at Mahout Meetup, April 17, 2014. 
+* Mahout Future Directions - Ted Dunning, Suneel Marthi, Sebastian Schelter at Hadoop Summit Europe 2014, Amsterdam, April 3, 2014
+* Building Recommender Systems for Mere-Mortals - Sebastian Schelter at Researchgate Developer Day, Berlin, November 2013
+* Recommendations with Apache Mahout - Sebastian Schelter at IBM Almaden Research Center, San Jose, September 2013
+* [Next Directions in Mahout’s Recommenders](http://de.slideshare.net/sscdotopen/next-directions-in-mahouts-recommenders) - Sebastian Schelter at Bay Area Mahout Meetup, Redwood City, August 2013 
+* [New Directions in Mahout’s Recommenders](http://de.slideshare.net/sscdotopen/new-directions-in-mahouts-recommenders) - Sebastian Schelter at Recommender Systems Get Together Berlin, April 2013
+* [Introduction to Mahout and Machine Learning](http://www.slideshare.net/VaradMeru/introduction-to-mahout-and-machine-learning) - Slides by Varad Meru, Software Development Engineer at Orzota. July 27th, 2013.
+* [An Introduction to Collaborative Filtering with Apache Mahout](http://de.slideshare.net/sscdotopen/introduction-to-collaborative-filtering-with-apache-mahout) - Sebastian Schelter at Recommender Systems Challenge Workshop in conjunction with ACM RecSys 2012, Dublin, September 2012
+* [How to build a recommender system based on Mahout and JavaEE](https://github.com/ManuelB/facebook-recommender-demo/raw/master/docs/Talk-BedCon-Berlin-2012.pdf) - Slides by Manuel Blechschmidt at Berlin Expert Days March, 2012.
+* [Apache Mahout for intelligent data analysis](http://lanyrd.com/2011/apachecon-north-america/skdtb/) - Slides from Isabel Drost at Apache Con NA November, 2011.
+* [Dr. Mahout: Analyzing clinical data using scalable and distributed computing](http://lanyrd.com/2011/apachecon-north-america/skdrk/) - Slides from Shannon Quinn at Apache Con NA November, 2011.
+* Frank Scholten at Berlin Buzzwords on June 7, 2011.
+* Introduction to Collaborative Filtering using Mahout (updated) - Talk by Sean Owen at the London Hadoop User Group on April 14, 2011.
+*  [Cool Tricks with Classifiers](http://www.meetup.com/LA-HUG/pages/Video_from_March_16th_LA-HUG_Ted_Dunning_Mahout) - Talk by Ted Dunning at the Los Angeles HUG talking about Mahout classifiers on March 16, 2011.
+* First Mahout Hackathon, Berlin, March 2011
+* [Mahout meetup](http://blog.jteam.nl/2011/01/13/announcement-lucene-nl-mahout-meetup-with-isabel-drost-feb-7/) - there were two talks at the Apache Mahout meetup at JTeam in Amsterdam, February 2011. [intro slides](http://isabel-drost.de/hadoop/slides/jteam.pdf)
+* [Mahout clustering](http://www.fosdem.org/2011/schedule/event/mahoutclustering.html) - Talk on Mahout clustering at data dev room FOSDEM, February 2011.
+* Scaling Data Analysis with Apache Mahout - talk on Mahout at O'Reilly Strata, February 2011. 
+* [Practical Machine Learning](http://www.slideshare.net/jaganadhg/mahout-tutorial-fossmeet-nitc) - Slides from Biju B and Jaganadh G, FOSSMEET-NITC, Calicut, India, February 2011.
+* [Mahout at AlphaCSPs The Edge 2010 (pdf)](http://www.javaedge.com/jedge/pdf/Mahout.pdf) - [slideshare](http://www.slideshare.net/arikogan/mahouts-presentation-at-alphacsps-the-edge-2010) - Slides from [Ariel Kogan](http://il.linkedin.com/in/arielkogan) AlphaCSP's The Edge, December 2010.
+* [Intelligent data analysis with Apache Mahout](http://isabel-drost.de/hadoop/slides/devoxx.pdf) - Slides from Isabel Drost, Devoxx Antwerp, November 2010.
+* [Apache Mahout introduction](http://isabel-drost.de/hadoop/slides/codebits.pdf) - Slides from Isabel Drost, codebits Lisbon, November 2010.
+* [Apache Mahout - Making Data Analysis Easy](http://isabel-drost.de/hadoop/slides/apachecon_2010.pdf) - Slides from Isabel Drost, Apache Con US Atlanta, November 2010.
+* [Practical Machine Learning](http://www.slideshare.net/jaganadhg/bck9) - Slides from Jaganadh G, BarCamp Kerala 9, November 2010.
+* [Mahout and its new classification framework](http://www.slideshare.net/tdunning/sdforum-11042010) - Slides from Ted Dunning, SDForum, November 2010.
+* [Distributed Item-based Collaborative Filtering with Apache Mahout](http://www.slideshare.net/sscdotopen/mahoutcf) - Slides from Sebastian Schelter, Hadoop Get Together Berlin, October 2010.
+* [Hidden Markov Models for Mahout](http://isabel-drost.de/hadoop/slides/HMM.pdf) - Slides from Max Heimel, Hadoop Get Together Berlin, October 2010.
+* [Apache Mahout Mammoth Scale Machine Learning](http://www.slideshare.net/robinanil/oscon-apache-mahout-mammoth-scale-machine-learning) - Slides from Robin Anil, OSCON 2010.
+* [Intro to Apache Mahout](http://slidesha.re/9LxOIu) - Slides from Grant Ingersoll,  RTP Semantic Web Group.
+* [Case study: Biometric Databases and Hadoop](http://www.slideshare.net/ydn/3-biometric-hadoopsummit2010) - Slides from Jason Trost, Hadoop Summit 2010.
+* [Spam Fighting at Yahoo](http://www.slideshare.net/hadoopusergroup/mail-antispam?from=ss_embed)
+* [Web Mining with Ken Krugler](http://www.slideshare.net/hadoopusergroup/bixo-hug-talk?from=ss_embed)
+* [Keynote on intelligent search](http://berlinbuzzwords.wikidot.com/local--files/links-to-slides/ingersoll_bbuzz2010.pdf) - Slides from Grant Ingersoll, Berlin Buzzwords, June 2010.
+* [Simple co-occurrence-based recommendation on Hadoop](http://berlinbuzzwords.wikidot.com/local--files/links-to-slides/owen_bbuzz2010.pdf) - Slides from Sean Owen, Berlin Buzzwords, June, 2010.
+* [Introduction to Collaborative Filtering using Mahout](http://berlinbuzzwords.wikidot.com/local--files/links-to-slides/scholten_bbuzz2010.odp) - Slides from Frank Scholten, Berlin Buzzwords, June, 2010.
+* [Introduction to Scalable Machine Learning](http://lucene.grantingersoll.com/2010/02/16/trijug-intro-to-mahout-slides-and-demo-examples/) - Slides and demos from Grant Ingersoll, March, 2010.
+* Mahout @ India Hadoop Summit - Slides from a 1 hour talk on Mahout at the India Hadoop Summit by Robin Anil, February 2010.
+* [Mahout in 10 minutes](http://www.isabel-drost.de/hadoop/slides/opensourceexpo09.pdf) - Slides from a 10 min intro to Mahout at the Map Reduce tutorial by David Zülke at Open Source Expo in Karlsruhe, Isabel Drost, November 2009.
+* [Mahout at Apache Con US](http://www.isabel-drost.de/hadoop/slides/apacheconus2009.pdf) - Slides from a talk on "Going from raw data to information" (with Mahout) at Apache Con US in Oakland, Isabel Drost, November 2009.
+* [Mahout at FrOSCon](http://www.isabel-drost.de/hadoop/slides/froscon2009.pdf) - Slides from a talk on Mahout at FrOSCon in Sankt Augustin, Isabel Drost, August 2009.
+* [Mahout at DAI group TU Berlin](http://www.isabel-drost.de/hadoop/slides/dai.pdf) - Slides from a talk on Mahout at the DAI Laboratories TU Berlin, Isabel Drost, July 2009.
+* [Mahout at Machine Learning Group TU Berlin](http://www.isabel-drost.de/hadoop/slides/ulf.pdf) - Slides from a talk on Hadoop with some detour to Mahout at the Machine Learning Group of Prof. Dr. Klaus-Robert Müller at TU Berlin, Isabel Drost, June 2009.
+* [Mahout at Google Zürich](http://www.isabel-drost.de/hadoop/slides/google.pdf) - Slides from a Google tech-talk on the past, present and future of Mahout, Isabel Drost, May 2009.
+* [Hadoop user group UK](http://static.last.fm/johan/huguk-20090414/isabel_drost-introducing_apache_mahout.pdf) - Slides from a talk on April 14, 2009 at the Hadoop User Group UK in London, Isabel Drost, April 2009.
+* [BI Over Petabytes: Meet Apache Mahout](http://cwiki.apache.org/confluence/download/attachments/88410/SDForum.pdf) - Slides from a talk by Jeff Eastman on April 21, 2009 at the Bay Area SD Forum Business Intelligence SIG meeting at SAP in Palo Alto, CA.
+* Lucene Meetup and Apache Barcamp in Amsterdam, March 2009.
+* BarCampRDU - (Raleigh) on Aug. 2, 2008
+* Introducing Mahout: Apache Machine Learning - Committer Grant Ingersoll gave a gentle introduction to Mahout and Machine Learning at ApacheCon in November (3rd through 7th) in New Orleans, USA. 
+* Mahout: Scaling Machine Learning - Introduction to Mahout and machine learning at FrOSCon in Sankt Augustin/Germany, Isabel Drost, August 2008.  ([slides](http://cwiki.apache.org/confluence/download/attachments/88410/froscon.pdf))
+* Mahout: Scalable Machine Learning - An introduction to Mahout and machine learning at the first German Hadoop gathering in newthinking store/ Berlin, Isabel Drost, July 2008.
+* Apache Mahout: Industrial Strength Machine Learning - Committer Jeff Eastman gave an introduction to Mahout at Yahoo!, May 2008
+* [Apache Lucene - Mach's wie Google](http://people.apache.org/~berndf/openexpode08-lucene-talk.pdf) - Bernd Fondermann presented an overview of the Apache Lucene project, including Mahout at Open Source Expo 2008 in Karlsruhe, May 2008.
+* Apache Mahout: Bringing Machine Learning to Industrial Strength - Committer Isabel Drost gave a Fast Feather introduction the the new project Mahout at Apache Con EU April, 2008
diff --git a/website/versioned_docs/version-0.6/learning/professional-support.md b/website/versioned_docs/version-0.6/learning/professional-support.md
new file mode 100644
index 000000000..5e1a656ca
--- /dev/null
+++ b/website/versioned_docs/version-0.6/learning/professional-support.md
@@ -0,0 +1,55 @@
+---
+title: Professional Support
+sidebar_label: Professional Support
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Professional Support for Mahout
+
+Add yourself or your company if you offer support, consulting, or training for Mahout users, whether for Qumat and QDP or for Mahout Classic. To be listed, open a pull request against this page or email dev@mahout.apache.org. Please keep the lists in alphabetical order.
+
+An entry here is not an endorsement by the Apache Software Foundation nor any of its committers.
+
+## People and companies for hire
+
+| Name | Contact details | Notes |
+|------|-----------------|-------|
+| Aboriginal Armadillo, LLC | trevor@aboriginal-armadillo.com | [Commercial support](http://www.aboriginal-armadillo.com/) |
+| Boston Predictive Analytics | sray@bostonpredictiveanalytics.com | [tutorteddy.com](http://tutorteddy.com/site/free_statistics_help.php) |
+| Frank Scholten | frank.scholten@orange11.nl | |
+| GridLine | [gridline.nl/contact](http://www.gridline.nl/contact) | Specialised in search and thesauri |
+| Jagdish Nomula | nomulaj@gmail.com | ML, search, algorithms, Java. [kosmex.com](http://www.kosmex.com) |
+| Jie-Kai Chang | jiekaichang@apache.org | Apache Mahout PMC Chair and QDP co-creator. GPU-accelerated quantum data encoding (CUDA/ROCm), zero-copy data pipelines, and Kubernetes-native ML infrastructure (Ray, KubeRay). [github.com/400Ping](https://github.com/400Ping) |
+| LucidWorks | [lucidworks.com](http://www.lucidworks.com) | Big data platform including Mahout as a service for clustering, classification and more |
+| Sematext International | [sematext.com](http://sematext.com/) | |
+| Ted Dunning | sales@maprtech.com | Full commercial support |
+| Winterwell | daniel@winterwell.com | Business and maths concept development, algorithms. [winterwell.com](http://winterwell.com) |
+
+## Talks and presentations
+
+People available to give talks about Mahout at meetups, conferences, and companies.
+
+| Name | Contact details | Notes |
+|------|-----------------|-------|
+| Andrew Musselman | akm@apache.org | ["Building a Recommender with Apache Mahout on Amazon Elastic-MapReduce"](https://blogs.aws.amazon.com/bigdata/post/Tx1TDK3HHBD4EZL/Building-a-Recommender-with-Apache-Mahout-on-Amazon-Elastic-MapReduce-EMR) |
+| Frank Scholten | frank@jteam.nl | Mahout/Taste. [blog.jteam.nl/author/frank](http://blog.jteam.nl/author/frank/) |
+| Isabel Drost-Fromm | isabel@apache.org | If travel and accommodation costs are covered, scheduling a talk is a lot easier. |
+| Jie-Kai Chang | jiekaichang@apache.org | Qumat and QDP, GPU-accelerated quantum data encoding. See [Community Over Code Asia 2026](https://asia.communityovercode.org/sessions/general-1194670.html). |
+
+The entries above date from the Mahout Classic era unless noted. Contact details were supplied by the people listed and may no longer be current.
diff --git a/website/versioned_docs/version-0.6/learning/reference-reading.md b/website/versioned_docs/version-0.6/learning/reference-reading.md
new file mode 100644
index 000000000..583466aeb
--- /dev/null
+++ b/website/versioned_docs/version-0.6/learning/reference-reading.md
@@ -0,0 +1,79 @@
+---
+title: Reference Reading
+sidebar_label: Reference Reading
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Reference Reading
+
+Here we provide references to books and courses about data analysis in general, which might also be helpful in the context of Mahout.
+
+### General Background Materials
+
+Don't be overwhelmed by all the maths, you can do a lot in Mahout with some
+basic knowledge. The books will help you understand your
+data better, and ask better questions both of Mahout's APIs, and also of
+the Mahout community. And unlike learning some particular software tool,
+these are skills that will remain useful decades later.
+* [Gilbert Strang](http://www-math.mit.edu/~gs)
+'s [Introduction to Linear Algebra](http://math.mit.edu/linearalgebra/). His [lectures](http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) are also [available online](http://web.mit.edu/18.06/www/)
+ and are strongly recommended. 
+* [Mathematical Tools for Applied Mulitvariate Analysis](http://www.amazon.com/Mathematical-Tools-Applied-Multivariate-Analysis/dp/0121609553/ref=sr_1_1?ie=UTF8&qid=1299602805&sr=8-1) by J.Douglass
+Carroll.
+* [Stanford Machine Learning online courseware](http://www.stanford.edu/class/cs229/)
+* [MIT Machine Learning online courseware](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/)  has [lecture notes](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/) online.
+* As a pre-requisite to probability and statistics, you'll need [basic calculus](http://en.wikipedia.org/wiki/Calculus). A maths for scientists text might be useful here such as 'Mathematics for Engineers and Scientists', Alan Jeffrey, Chapman & Hall/CRC. ([openlibrary](http://openlibrary.org/books/OL3305993M/Mathematics_for_engineers_and_scientists))
+* One of the best writers in the probability/statistics world is Sheldon Ross. Try [A First Course in Probability (8th Edition)](http://www.pearsonhighered.com/educator/product/First-Course-in-Probability-A/9780136033134.page) and then move on to his [Introduction to Probability Models](http://www.amazon.com/Introduction-Probability-Models-Sixth-Sheldon/dp/0125984707)
+
+Some good introductory alternatives here are:
+* [Kahn Academy](http://www.khanacademy.org/) -- videos on stats, probability, linear algebra
+* [Probability and Statistics (7th Edition)](http://www.amazon.com/Probability-Statistics-Engineering-Sciences-InfoTrac/dp/0534399339), Jay L. Devore, Chapman.
+* [Probability and Statistical Inference (7th Edition)](http://www.amazon.com/Probability-Statistical-Inference-Robert-Hogg/dp/0132546086), Hogg and Tanis, Pearson.
+
+Once you have a grasp of the basics then there are a slew of great texts that you might consult:
+* [Statistical Inference](http://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126), Casell and Berger, Duxbury/Thomson Learning.
+* [Introduction to Bayesian Statistics](http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0471270202), William H. Bolstad, Wiley. 
+* [Understanding Computational Bayesian Statistics](http://www.amazon.com/Understanding-Computational-Bayesian-Statistics-Wiley/dp/0470046090), Bolstadt
+* [Bayesian Data Analysis, Gelman et al.](http://www.stat.columbia.edu/~gelman/book/)
+
+### For statistics related to machine learning, these are particularly helpful:
+
+* [Pattern Recognition and Machine Learning by Chris Bishop](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
+* [Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) by Trevor Hastie, Robert Tibshirani, Jerome Friedman 
+* [http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm](http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm)
+ 
+
+### For matrix computations/decomposition/factorization etc.:
+
+* Peter V. O'Neil [Introduction to Linear Algebra](http://www.amazon.com/Introduction-Linear-Algebra-Theory-Applications/dp/053400606X), great book for beginners (with some knowledge in calculus). It is not comprehensive, but, it will be a good place to start and the author starts by explaining the concepts with regards to vector spaces which I found to be a more natural way of explaining.
+* David S. Watkins [Fundamentals of Matrix Computations](http://www.amazon.com/Fundamentals-Matrix-Computations-Applied-Mathematics/dp/0470528338/)
+* [Matrix Computations](http://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/0801854148/ref=sr_1_2?s=books&ie=UTF8&qid=1394307676&sr=1-2&keywords=golub+van+loan) is the classic text for numerical linear algebra. Can't go wrong with it - great for researchers.  
+* Nick Trefethen's [Numerical Linear Algebra](http://people.maths.ox.ac.uk/trefethen/books.html).  It's a bit more approachable for practitioners. Many chapters on SVD, there are even chapters on Lanczos.
+
+### Books specifically on R:
+
+* Learning about R is a difficult thing. The best introduction is in MASS [http://www.stats.ox.ac.uk/pub/MASS4/](http://www.stats.ox.ac.uk/pub/MASS4/)
+* [R Tutor](http://www.r-tutor.com/r-introduction)
+* [Manual](http://cran.r-project.org/doc/manuals/R-intro.pdf)
+* [R Course](http://faculty.washington.edu/tlumley/Rcourse/)
+
+In addition, you should see how to plot data well:
+
+* [Trellis plotting](http://www.statmethods.net/advgraphs/trellis.html)
+* [ggplot2](http://had.co.nz/ggplot2/)
diff --git a/website/versioned_sidebars/version-0.6-sidebars.json b/website/versioned_sidebars/version-0.6-sidebars.json
index 25312c23a..180c811d2 100644
--- a/website/versioned_sidebars/version-0.6-sidebars.json
+++ b/website/versioned_sidebars/version-0.6-sidebars.json
@@ -95,6 +95,9 @@
         "learning/papers/Unleashing-the-Potential-of-LLMs-for-Quantum-Computing"
       ]
     },
+    "learning/books-tutorials-and-talks",
+    "learning/reference-reading",
+    "learning/professional-support",
     {
       "type": "category",
       "label": "Community",