Jan 11, 2022
Home Automation: What works for me
Home automation has been an interest of mine for a while. Here, I collect notes on what worked for me. Both as a reminder for myself and in the hopes that it is useful to others dabbling in the space. This is not a tutorial for any of the devices mentioned. There are plenty of those available on the internet.Read more...
Sep 15, 2021
PerfGuard: deploying ML-for-systems without performance regressions, almost!
Remmelt Ammerlaan, Gilbert Antonius, Marc Friedman, H M Sajjad Hossain, Alekh Jindal, Peter Orenberg, Hiren Patel, Shi Qiao, Vijay Ramani, Lucas Rosenblatt, Abhishek Roy, Irene Shaffer, Soundarajan Srinivasan, Markus WeimerRead more...
Apr 15, 2021
FLAML: A Fast and Lightweight AutoML Library
Chi Wang, Qingyun Wu, Markus Weimer, Erkang ZhuRead more...
Nov 5, 2020
A Tensor Compiler for Unified Machine Learning Prediction Serving
Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo InterlandiRead more...
Oct 19, 2020
Connecting a Webcam and USB Audio to a Hyper-V VM
Update (2012-12-19): I am currently unable to use the method below for the combination of Windows 11 Host and Windows 11 Client OS. I am looking for a new software solution. For now, I am back to using a USB device server on my network for this.Read more...
Aug 23, 2020
Building Continuous Integration Services for Machine Learning
Bojan Karlaš, Matteo Interlandi, Cedric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus WeimerRead more...
Aug 1, 2020
Vamsa: Automated Provenance Tracking in Data Science Scripts
Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yinghui Wu, Yiwen Zhu, Markus WeimerRead more...
Jul 17, 2020
MLOS: An Infrastructure for Automated Software Performance Engineering
Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Siqi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen ZhuRead more...
Jun 23, 2020
Git on the Windows Subsystem for Linux
I do most of my dev work on WSL, with the help of some nice integrations with the Windows side of the machine. I run both VS Code and the Git credential manager on Windows, called from within Linux:Read more...
Jan 15, 2020
Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Gowdal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen ZhuRead more...
Jan 15, 2020
Extending Relational Query Processing with ML Inference
Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo CurinoRead more...
Dec 27, 2019
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet TalwalkarRead more...
Jun 15, 2019
My talk at ICML 2019
Today, I gave a talk at the Coding Theory for Large-Scale Machine Learning Workshop at ICML 2019. You can find the slides below, or download them: PDF|PPTXRead more...
- Apr 4, 2019 My talk at XLDB 2019 Read more...
Dec 20, 2018
From the Edge to the Cloud: Model Serving in ML.NET
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo InterlandiRead more...
- Dec 12, 2018 TVM and Apache
Nov 19, 2018
Papers accepted to NeurIPS Workshops 2018
I am happy to announce that my team has five papers accepted in the workshop section of NeurIPS this year:Read more...
Oct 23, 2018
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo InterlandiRead more...
Sep 12, 2018
Test of Time Award at ECML/PKDD 2018
I am honored and humbled to receive the Test of Time Award together with Alex Smola and Alexandros Karatzoglou. It is given to our work on matrix factorization you can find here on my website. Here is the deck Alex used at ECML/PKDD 2018 today in his talk to commemorate the work, give it perspective with the field since and produce some ideas of where the field might go.
Feb 7, 2018
Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus WeimerRead more...
Dec 23, 2017
How to fix the Anaconda Prompt installed by Visual Studio 2017
Visual Studio now can install Anaconda’s Python distribution. Awesome! What isn’t so awesome: TheRead more...
Anaconda Promptin the start menu that installs doesn’t work. When I oppened it, I was greeted with:
Dec 15, 2017
Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola, Arvind KrishnamurthyRead more...
Nov 16, 2017
Towards High-Performance Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Matteo Interlandi, Markus Weimer, Byung-Gon ChunRead more...
Oct 31, 2017
Apache REEF: Retainable Evaluator Execution Framework
Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Matteo Interlandi, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok YangRead more...
Oct 31, 2016
We're hiring Machine Learning Researchers
Are you amazed by the progress machine learning has made over the past few years? Are you at the same time frustrated that so relatively few organizations in the world can benefit from ML? Do you read NIPS, COLT, ICML and KDD papers for pleasure? If you answered yes to these I want to talk to you. Apply within.
Jun 28, 2016
Get paid to work on Apache REEF
The team dedicated to Apache REEF at Microsoft is hiring. Apply within :-)
Jun 22, 2016
Salmon: Towards Production-Grade, Platform-Independent Distributed ML
Mikhail Bilenko, Tom Finley, Shon Katzenberger, Sebastian Kochman, Dhruv Mahajan, Shravan Narayanamurthy, Julia Wang, Shizhen Wang, Markus WeimerRead more...
Jun 22, 2016
Dolphin: Runtime Optimization for Distributed Machine Learning
Byung-Gon Chun, Brian Cho, Beomyeol Jeon, Joo Seong Jeong, Gunhee Kim, Joo Yeon Kim, Woo-Yeon Lee, Yun Seong Lee, Markus Weimer, Youngseok Yang, Gyeong-In YuRead more...
May 25, 2016
Validating checksums in PowerShell
In this post, I describe to verifyRead more...
.SHA512hashes with PowerShell, without any additional tools.
May 25, 2016
Twitter's Heron, powered by Apache REEF
Today, Twitter announced the open sourcing of Heron, their successor to Apache Storm. Heron’s Apache Hadoop YARN port is built using Apache REEF. I’m excited and looking forward to the interesting edge cases they’ll report over on the dev list :-)
May 14, 2016
Apache REEF @ Apache Big Data 2016
Sergiy Matusevych presented Apache REEF at Apache Big Data 2016 in Vancouver, BC. Find the slides below.Read more...
Mar 14, 2016
How to make a C++ project compile in Visual Studio 2013 and 2015
Over in Apache REEF, we needed to make our solution compile in both Visual Studio 2013 and 2015. The main impediment to that was the C++ code in the project, which needs to be compiled with the “right” compiler, depending on the version of Visual Studio used. Below, I describe how we created a project file that can be compiled uniformly across Visual Studio versions.Read more...
Feb 3, 2016
Better code reviews for Apache REEF
Using GitHub and Apache’s JIRA for code and issue management can be a bit of a pain. I started a Firefox extension that helps a bit with is. For now, it only adds the JIRA link to the Pull Requests overview page. More to come in the future. Download Version 0.0.1, Source.
Dec 12, 2015
Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino and Giovanni Matteo FumarolaRead more...
Nov 15, 2015
Slides from my talks in Europe in October
Below, you find my slides from my talks in Europe. You can also download them as a PPTX or PDF using the controls at the bottom of the frame.Read more...
Sep 30, 2015
Behind the scenes of Azure Data Lake - Apache REEF
Raghu Ramakrishnan just posted about what we’ve been up to at Microsoft in the last few years:Read more...
Sep 7, 2015
Europe October 2015
During October 2015, I am visiting several European universities and academic institutions.Read more...
Sep 5, 2015
I’ve decided to move this page to Jekyll and have it hosted on GitHub. For the next couple of weeks, this likely means that the page will be riddled with dead links. Stay tuned…
Sep 1, 2015
Learning Systems 2015 CFP is up
The call for papers for the Learning Systems workshop at NIPS 2015 has been posted:Read more...
Jun 2, 2015
REEF: Retainable Evaluator Execution Framework
Markus Weimer, Yingda Chen, Byung-Gon Chun, Tyson Condie, Carlo Curino, Chris Douglas, Yunseong Lee, Tony Majestro, Dahlia Malkhi , Sergiy Matusevych, Brandon Myers, Shravan Narayanamurthy, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Julia WangRead more...
Dec 12, 2014
Elastic Distributed Bayesian Collaborative Filtering
Alex Beutel, Markus Weimer, Tom Minka, Yordan Zaykov, Vijay NarayananRead more...
Jan 9, 2014
Towards Resource-Elastic Machine Learning
Shravan Narayanamurthy, Markus Weimer, Dhruv Mahajan, Tyson Condie, Sundararajan Sellamanickam, Keerthi SelvarajRead more...
Jan 9, 2014
Distributed and Scalable PCA in the Cloud
Arun Kumar, Nikos Karampatziakis, Paul Mineiro, Markus Weimer and Vijay NarayananRead more...
Aug 2, 2013
How to setup PowerShell for GitHub, Maven and Java development
This post has been replaced by the guid in the Apache REEF wiki
- Jul 5, 2013 Tutorial: Machine Learning on Big Data (SIGMOD 2013) Read more...
- Apr 24, 2013 ICDE Tutorial on Machine Learning on Big Data
Apr 4, 2013
G'day mates: Australia April 2013
I am travelling in Australia for a few days. I can be found in these locations:Read more...
Sep 7, 2012
CFP: Big Learning 2012: Algorithms, Systems and Tools
NIPS 2012 Workshophttp://www.biglearn.orgRead more...
Jul 1, 2012
Declarative Systems for Large-Scale Machine Learning
Vinayak Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer and Raghu RamakrishnanRead more...
- Jun 6, 2012 KDD Cup 2011 proceedings online
May 21, 2012
A last yodel
Friday was my last day at Yahoo!Read more...
Apr 16, 2012
Talk at Berlin Buzzwords
I’ve been invited to talk about our work on ScalOps at Berlin Buzzwords! According to the Schedule, I’ll be speaking June 4th, 17:04 - 17:45 in the room “Kleistsaal”.Read more...
- Apr 6, 2012 WWW 2012 Tutorial: New Templates for Scalable Data Analysis Read more...
Nov 21, 2011
Machine learning in ScalOps, a higher order cloud computing language
Markus Weimer, Tyson Condie and Raghu RamakrishnanRead more...
Sep 1, 2011
CFP: Big Learning Workshop
Big Learning: Algorithms, Systems, and Tools for Learning at ScaleRead more...
Aug 21, 2011
The Yahoo! Music Dataset and KDD-Cup’11
Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus WeimerRead more...
Mar 16, 2011
Back to CS 101
So, you read Alex’ post on applying the hashing trick to matrix factorization or our paper on the subject and thought you’d just go ahead and implement it. And instead of computing two hashes, one for the Rademacher function ( [latex]\sigma[/latex] in the paper) and one for the index in the storage array, you compute just one and use the sign of the hash as [latex]\sigma[/latex] and the absolute value for the index. Which looks something like this in Java:Read more...
Dec 11, 2010
A Convenient Framework for Efficient Parallel Multipass Algorithms
Markus Weimer, Sriram Rao, Martin ZinkevichRead more...
Dec 9, 2010
Parallelized Stochastic Gradient Descent
Martin Zinkevich, Markus Weimer, Alex Smola, Lihong LiRead more...
Oct 25, 2010
KDD Cup 2011 - Organized by Yahoo!
I’m happy to announce that we are organizing KDD Cup this year. Read more at kdd.org
Aug 30, 2010
Quantile Matrix Factorization for Collaborative Filtering
Alexandros Karatzoglou, Markus WeimerRead more...
May 12, 2010
Collaborative Filtering on a Budget
Alexandros Karatzoglou, Alex Smola, Markus WeimerRead more...
Feb 26, 2010
Yahoo! Learning To Rank Challenge
Yahoo! is running a learning to rank challenge. So finally, we can see a fair comparison between all the different approaches to learning to rank.Read more...
Feb 12, 2010
MLOSS 2010 – ICML Workshop just accepted
We are glad to announce that our MLOSS 2010 workshop at this years ICML conference has been accepted!
We are therefore happily accepting software submissions. The deadline for the submissions is April 10th, 2010. If accepted, you can present your software to the workshop audience, which is a great opportunity to make your piece of software more known to the machine learning community.
Like last time, we will use mloss.org for managing the submissions. You basically just have to register your project with mloss.org and add the tag
icml2010to it. For more information, have a look at the workshop page.
Jan 18, 2010
Machine Teaching -- A Machine Learning Approach to Technology Enhanced Learning
Markus WeimerRead more...
Oct 25, 2009
Maximum margin matrix factorization for code recommendation
Markus Weimer, Alexandros Karatzoglou and Marcel BruchRead more...
Jun 30, 2009
Call for Papers – Special Issue of the “Journal of Web Semantics”
I’m a reviewer of the Special Issue of the Journal of Web Semantics on “Bridging the Gap” - Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0. Here is the call for papers.
Mar 30, 2009
CofiRank 0.1 released
We just released version 0.1 of cofirank, the implementation of our algorithm of the same name.Read more...
Feb 11, 2009
Upcoming Conference: iatel
My grad school is organizing a conference next summer on e-learning – technology enhanced learning for those from across the ocean. You might think “yawn! yet another conference!”, but this one is special:Read more...
Oct 25, 2008
Adaptive Collaborative Filtering
Markus Weimer, Alexandros Karatzoglou and Alexander J. SmolaRead more...
Sep 30, 2008
Optimization of the NDCG ranking score
A few people contacted me with the question how the NDCG optimization in our NIPS07 paper on collaborative ranking is done in detail. Unfortunately, we did not manage to squeeze these details into the paper itself. Luckily, there is the internet and I just uploaded my notes on this to the CofiRank site. Please note that these notes are not peer reviewed. If you find a bug/typo/whatever in the derivations, I would be more than happy to hear from you!
Sep 19, 2008
Improving Maximum Margin Matrix Factorization
Markus Weimer, Alexandros Karatzoglou and Alexander J. SmolaRead more...
Dec 17, 2007
CofiRank - Maximum Margin Matrix Factorization for Collaborative Ranking
Markus Weimer, Alexandros Karatzoglou, Quoc Viet Le, Alex SmolaRead more...