+.. title:: Testimonials +.. _testimonials: -.. to add a testimonials, just XXX +========================== +Who is using scikit-learn? +========================== `J.P.Morgan `_ ------------------------------------------- - -.. raw:: html - -

- -Scikit-learn is an indispensable part of the Python machine learning -toolkit at JPMorgan. It is very widely used across all parts of the bank -for classification, predictive analytics, and very many other machine -learning tasks. Its straightforward API, its breadth of algorithms, and -the quality of its documentation combine to make scikit-learn -simultaneously very approachable and very powerful. +---------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Stephen Simmons, VP, Athena Research, JPMorgan + Scikit-learn is an indispensable part of the Python machine learning + toolkit at JPMorgan. It is very widely used across all parts of the bank + for classification, predictive analytics, and very many other machine + learning tasks. Its straightforward API, its breadth of algorithms, and + the quality of its documentation combine to make scikit-learn + simultaneously very approachable and very powerful. -.. raw:: html + .. rst-class:: annotation - -

+ Stephen Simmons, VP, Athena Research, JPMorgan -.. image:: images/jpmorgan.png - :width: 120pt - :align: center - :target: https://www.jpmorgan.com + .. div:: image-box -.. raw:: html + .. image:: images/jpmorgan.png + :target: https://www.jpmorgan.com -

`Spotify `_ ------------------------------------ -.. raw:: html - -

- -Scikit-learn provides a toolbox with solid implementations of a bunch of -state-of-the-art models and makes it easy to plug them into existing -applications. We've been using it quite a lot for music recommendations at -Spotify and I think it's the most well-designed ML package I've seen so -far. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify + Scikit-learn provides a toolbox with solid implementations of a bunch of + state-of-the-art models and makes it easy to plug them into existing + applications. We've been using it quite a lot for music recommendations at + Spotify and I think it's the most well-designed ML package I've seen so far. -.. raw:: html + .. rst-class:: annotation - -

+ Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify -.. image:: images/spotify.png - :width: 120pt - :align: center - :target: https://www.spotify.com + .. div:: image-box -.. raw:: html + .. image:: images/spotify.png + :target: https://www.spotify.com -

`Inria `_ -------------------------------- -.. raw:: html - -

- -.. title Scikit-learn for efficient and easier machine learning research -.. Author: Gaël Varoquaux - - -At INRIA, we use scikit-learn to support leading-edge basic research in many -teams: `Parietal `_ for neuroimaging, `Lear -`_ for computer vision, `Visages -`_ for medical image analysis, `Privatics -`_ for security. The project is a fantastic -tool to address difficult applications of machine learning in an academic -environment as it is performant and versatile, but all easy-to-use and well -documented, which makes it well suited to grad students. +.. div:: sk-text-image-grid-large + .. div:: text-box -.. raw:: html + At INRIA, we use scikit-learn to support leading-edge basic research in many + teams: `Parietal `_ for neuroimaging, `Lear + `_ for computer vision, `Visages + `_ for medical image analysis, `Privatics + `_ for security. The project is a fantastic + tool to address difficult applications of machine learning in an academic + environment as it is performant and versatile, but all easy-to-use and well + documented, which makes it well suited to grad students. - + .. rst-class:: annotation -Gaël Varoquaux, research at Parietal + Gaël Varoquaux, research at Parietal -.. raw:: html + .. div:: image-box - -

- -.. image:: images/inria.png - :width: 120pt - :align: center - :target: https://www.inria.fr/ - -.. raw:: html - -

+ .. image:: images/inria.png + :target: https://www.inria.fr/ `betaworks `_ ------------------------------------ -.. raw:: html - -

- -Betaworks is a NYC-based startup studio that builds new products, grows -companies, and invests in others. Over the past 8 years we've launched a -handful of social data analytics-driven services, such as Bitly, Chartbeat, -digg and Scale Model. Consistently the betaworks data science team uses -Scikit-learn for a variety of tasks. From exploratory analysis, to product -development, it is an essential part of our toolkit. Recent uses are included -in `digg's new video recommender system -`_, -and Poncho's `dynamic heuristic subspace clustering -`_. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + Betaworks is a NYC-based startup studio that builds new products, grows + companies, and invests in others. Over the past 8 years we've launched a + handful of social data analytics-driven services, such as Bitly, Chartbeat, + digg and Scale Model. Consistently the betaworks data science team uses + Scikit-learn for a variety of tasks. From exploratory analysis, to product + development, it is an essential part of our toolkit. Recent uses are included + in `digg's new video recommender system + `_, + and Poncho's `dynamic heuristic subspace clustering + `_. -Gilad Lotan, Chief Data Scientist + .. rst-class:: annotation -.. raw:: html + Gilad Lotan, Chief Data Scientist - -

+ .. div:: image-box -.. image:: images/betaworks.png - :width: 120pt - :align: center - :target: https://betaworks.com - -.. raw:: html - -

+ .. image:: images/betaworks.png + :target: https://betaworks.com `Hugging Face `_ ---------------------------------------- -.. raw:: html - -

- -At Hugging Face we're using NLP and probabilistic models to generate -conversational Artificial intelligences that are fun to chat with. Despite using -deep neural nets for `a few `_ -of our `NLP tasks `_, scikit-learn is still the bread-and-butter of -our daily machine learning routine. The ease of use and predictability of the -interface, as well as the straightforward mathematical explanations that are -here when you need them, is the killer feature. We use a variety of scikit-learn -models in production and they are also operationally very pleasant to work with. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + At Hugging Face we're using NLP and probabilistic models to generate + conversational Artificial intelligences that are fun to chat with. Despite using + deep neural nets for `a few `_ + of our `NLP tasks `_, scikit-learn is still the + bread-and-butter of our daily machine learning routine. The ease of use and + predictability of the interface, as well as the straightforward mathematical + explanations that are here when you need them, is the killer feature. We use a + variety of scikit-learn models in production and they are also operationally very + pleasant to work with. -Julien Chaumond, Chief Technology Officer + .. rst-class:: annotation -.. raw:: html + Julien Chaumond, Chief Technology Officer - -

+ .. div:: image-box -.. image:: images/huggingface.png - :width: 120pt - :align: center - :target: https://huggingface.co - -.. raw:: html - -

+ .. image:: images/huggingface.png + :target: https://huggingface.co `Evernote `_ ---------------------------------- -.. raw:: html - -

- -Building a classifier is typically an iterative process of exploring -the data, selecting the features (the attributes of the data believed -to be predictive in some way), training the models, and finally -evaluating them. For many of these tasks, we relied on the excellent -scikit-learn package for Python. +.. div:: sk-text-image-grid-large -`Read more `_ + .. div:: text-box -.. raw:: html + Building a classifier is typically an iterative process of exploring + the data, selecting the features (the attributes of the data believed + to be predictive in some way), training the models, and finally + evaluating them. For many of these tasks, we relied on the excellent + scikit-learn package for Python. - + `Read more `_ -Mark Ayzenshtat, VP, Augmented Intelligence + .. rst-class:: annotation -.. raw:: html + Mark Ayzenshtat, VP, Augmented Intelligence - -

+ .. div:: image-box -.. image:: images/evernote.png - :width: 120pt - :align: center - :target: https://evernote.com + .. image:: images/evernote.png + :target: https://evernote.com -.. raw:: html - -

`Télécom ParisTech `_ -------------------------------------------------------- -.. raw:: html - -

- -At Telecom ParisTech, scikit-learn is used for hands-on sessions and home -assignments in introductory and advanced machine learning courses. The classes -are for undergrads and masters students. The great benefit of scikit-learn is -its fast learning curve that allows students to quickly start working on -interesting and motivating problems. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Alexandre Gramfort, Assistant Professor + At Telecom ParisTech, scikit-learn is used for hands-on sessions and home + assignments in introductory and advanced machine learning courses. The classes + are for undergrads and masters students. The great benefit of scikit-learn is + its fast learning curve that allows students to quickly start working on + interesting and motivating problems. -.. raw:: html + .. rst-class:: annotation - -

+ Alexandre Gramfort, Assistant Professor -.. image:: images/telecomparistech.jpg - :width: 120pt - :align: center - :target: https://www.telecom-paristech.fr/ + .. div:: image-box -.. raw:: html - -

+ .. image:: images/telecomparistech.jpg + :target: https://www.telecom-paristech.fr/ `Booking.com `_ ------------------------------------------ -.. raw:: html - -

- -At Booking.com, we use machine learning algorithms for many different -applications, such as recommending hotels and destinations to our customers, -detecting fraudulent reservations, or scheduling our customer service agents. -Scikit-learn is one of the tools we use when implementing standard algorithms -for prediction tasks. Its API and documentations are excellent and make it easy -to use. The scikit-learn developers do a great job of incorporating state of -the art implementations and new algorithms into the package. Thus, scikit-learn -provides convenient access to a wide spectrum of algorithms, and allows us to -readily find the right tool for the right job. - +---------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Melanie Mueller, Data Scientist + At Booking.com, we use machine learning algorithms for many different + applications, such as recommending hotels and destinations to our customers, + detecting fraudulent reservations, or scheduling our customer service agents. + Scikit-learn is one of the tools we use when implementing standard algorithms + for prediction tasks. Its API and documentations are excellent and make it easy + to use. The scikit-learn developers do a great job of incorporating state of + the art implementations and new algorithms into the package. Thus, scikit-learn + provides convenient access to a wide spectrum of algorithms, and allows us to + readily find the right tool for the right job. -.. raw:: html + .. rst-class:: annotation - -

+ Melanie Mueller, Data Scientist -.. image:: images/booking.png - :width: 120pt - :align: center - :target: https://www.booking.com + .. div:: image-box -.. raw:: html + .. image:: images/booking.png + :target: https://www.booking.com -

`AWeber `_ ------------------------------------------- - -.. raw:: html - -

- -The scikit-learn toolkit is indispensable for the Data Analysis and Management -team at AWeber. It allows us to do AWesome stuff we would not otherwise have -the time or resources to accomplish. The documentation is excellent, allowing -new engineers to quickly evaluate and apply many different algorithms to our -data. The text feature extraction utilities are useful when working with the -large volume of email content we have at AWeber. The RandomizedPCA -implementation, along with Pipelining and FeatureUnions, allows us to develop -complex machine learning algorithms efficiently and reliably. +----------------------------------- -Anyone interested in learning more about how AWeber deploys scikit-learn in a -production environment should check out talks from PyData Boston by AWeber's -Michael Becker available at https://github.com/mdbecker/pydata_2013 +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + The scikit-learn toolkit is indispensable for the Data Analysis and Management + team at AWeber. It allows us to do AWesome stuff we would not otherwise have + the time or resources to accomplish. The documentation is excellent, allowing + new engineers to quickly evaluate and apply many different algorithms to our + data. The text feature extraction utilities are useful when working with the + large volume of email content we have at AWeber. The RandomizedPCA + implementation, along with Pipelining and FeatureUnions, allows us to develop + complex machine learning algorithms efficiently and reliably. -Michael Becker, Software Engineer, Data Analysis and Management Ninjas + Anyone interested in learning more about how AWeber deploys scikit-learn in a + production environment should check out talks from PyData Boston by AWeber's + Michael Becker available at https://github.com/mdbecker/pydata_2013. -.. raw:: html + .. rst-class:: annotation - -

+ Michael Becker, Software Engineer, Data Analysis and Management Ninjas -.. image:: images/aweber.png - :width: 120pt - :align: center - :target: https://www.aweber.com/ + .. div:: image-box -.. raw:: html + .. image:: images/aweber.png + :target: https://www.aweber.com -

`Yhat `_ ------------------------------------------- - -.. raw:: html +------------------------------ -

+.. div:: sk-text-image-grid-large -The combination of consistent APIs, thorough documentation, and top notch -implementation make scikit-learn our favorite machine learning package in -Python. scikit-learn makes doing advanced analysis in Python accessible to -anyone. At Yhat, we make it easy to integrate these models into your production -applications. Thus eliminating the unnecessary dev time encountered -productionizing analytical work. + .. div:: text-box + The combination of consistent APIs, thorough documentation, and top notch + implementation make scikit-learn our favorite machine learning package in + Python. scikit-learn makes doing advanced analysis in Python accessible to + anyone. At Yhat, we make it easy to integrate these models into your production + applications. Thus eliminating the unnecessary dev time encountered + productionizing analytical work. -.. raw:: html + .. rst-class:: annotation - + Greg Lamp, Co-founder -Greg Lamp, Co-founder Yhat + .. div:: image-box -.. raw:: html + .. image:: images/yhat.png + :target: https://www.yhat.com - -

- -.. image:: images/yhat.png - :width: 120pt - :align: center - :target: https://www.yhat.com - -.. raw:: html - -

`Rangespan `_ ----------------------------------------- +--------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -

+ .. div:: text-box -The Python scikit-learn toolkit is a core tool in the data science -group at Rangespan. Its large collection of well documented models and -algorithms allow our team of data scientists to prototype fast and -quickly iterate to find the right solution to our learning problems. -We find that scikit-learn is not only the right tool for prototyping, -but its careful and well tested implementation give us the confidence -to run scikit-learn models in production. + The Python scikit-learn toolkit is a core tool in the data science + group at Rangespan. Its large collection of well documented models and + algorithms allow our team of data scientists to prototype fast and + quickly iterate to find the right solution to our learning problems. + We find that scikit-learn is not only the right tool for prototyping, + but its careful and well tested implementation give us the confidence + to run scikit-learn models in production. -.. raw:: html + .. rst-class:: annotation - + Jurgen Van Gael, Data Science Director -Jurgen Van Gael, Data Science Director at Rangespan Ltd + .. div:: image-box -.. raw:: html + .. image:: images/rangespan.png + :target: http://www.rangespan.com - -

- -.. image:: images/rangespan.png - :width: 120pt - :align: center - :target: http://www.rangespan.com - -.. raw:: html - -

`Birchbox `_ ------------------------------------------- - -.. raw:: html - -

- -At Birchbox, we face a range of machine learning problems typical to -E-commerce: product recommendation, user clustering, inventory prediction, -trends detection, etc. Scikit-learn lets us experiment with many models, -especially in the exploration phase of a new project: the data can be passed -around in a consistent way; models are easy to save and reuse; updates keep us -informed of new developments from the pattern discovery research community. -Scikit-learn is an important tool for our team, built the right way in the -right language. - -.. raw:: html +-------------------------------------- - +.. div:: sk-text-image-grid-large -Thierry Bertin-Mahieux, Birchbox, Data Scientist + .. div:: text-box -.. raw:: html + At Birchbox, we face a range of machine learning problems typical to + E-commerce: product recommendation, user clustering, inventory prediction, + trends detection, etc. Scikit-learn lets us experiment with many models, + especially in the exploration phase of a new project: the data can be passed + around in a consistent way; models are easy to save and reuse; updates keep us + informed of new developments from the pattern discovery research community. + Scikit-learn is an important tool for our team, built the right way in the + right language. - -

+ .. rst-class:: annotation -.. image:: images/birchbox.jpg - :width: 120pt - :align: center - :target: https://www.birchbox.com + Thierry Bertin-Mahieux, Data Scientist -.. raw:: html + .. div:: image-box -

+ .. image:: images/birchbox.jpg + :target: https://www.birchbox.com `Bestofmedia Group `_ --------------------------------------------------- +------------------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -

+ .. div:: text-box -Scikit-learn is our #1 toolkit for all things machine learning -at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting, -ad click prediction, various ranking models) thanks to the varied, -state-of-the-art algorithm implementations packaged into it. -In the lab it accelerates prototyping of complex pipelines. In -production I can say it has proven to be robust and efficient enough -to be deployed for business critical components. + Scikit-learn is our #1 toolkit for all things machine learning + at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting, + ad click prediction, various ranking models) thanks to the varied, + state-of-the-art algorithm implementations packaged into it. + In the lab it accelerates prototyping of complex pipelines. In + production I can say it has proven to be robust and efficient enough + to be deployed for business critical components. -.. raw:: html + .. rst-class:: annotation - + Eustache Diemert, Lead Scientist -Eustache Diemert, Lead Scientist Bestofmedia Group + .. div:: image-box -.. raw:: html + .. image:: images/bestofmedia-logo.png + :target: http://www.bestofmedia.com - -

- -.. image:: images/bestofmedia-logo.png - :width: 120pt - :align: center - :target: http://www.bestofmedia.com - -.. raw:: html - -

`Change.org `_ -------------------------------------- -.. raw:: html - -

+.. div:: sk-text-image-grid-large -At change.org we automate the use of scikit-learn's RandomForestClassifier -in our production systems to drive email targeting that reaches millions -of users across the world each week. In the lab, scikit-learn's ease-of-use, -performance, and overall variety of algorithms implemented has proved invaluable -in giving us a single reliable source to turn to for our machine-learning needs. + .. div:: text-box -.. raw:: html + At change.org we automate the use of scikit-learn's RandomForestClassifier + in our production systems to drive email targeting that reaches millions + of users across the world each week. In the lab, scikit-learn's ease-of-use, + performance, and overall variety of algorithms implemented has proved invaluable + in giving us a single reliable source to turn to for our machine-learning needs. - + .. rst-class:: annotation -Vijay Ramesh, Software Engineer in Data/science at Change.org + Vijay Ramesh, Software Engineer in Data/science at Change.org -.. raw:: html + .. div:: image-box - -

+ .. image:: images/change-logo.png + :target: https://www.change.org -.. image:: images/change-logo.png - :width: 120pt - :align: center - :target: https://www.change.org - -.. raw:: html - -

`PHIMECA Engineering `_ ----------------------------------------------------------- - -.. raw:: html - -

- -At PHIMECA Engineering, we use scikit-learn estimators as surrogates for -expensive-to-evaluate numerical models (mostly but not exclusively -finite-element mechanical models) for speeding up the intensive post-processing -operations involved in our simulation-based decision making framework. -Scikit-learn's fit/predict API together with its efficient cross-validation -tools considerably eases the task of selecting the best-fit estimator. We are -also using scikit-learn for illustrating concepts in our training sessions. -Trainees are always impressed by the ease-of-use of scikit-learn despite the -apparent theoretical complexity of machine learning. +--------------------------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Vincent Dubourg, PHIMECA Engineering, PhD Engineer + At PHIMECA Engineering, we use scikit-learn estimators as surrogates for + expensive-to-evaluate numerical models (mostly but not exclusively + finite-element mechanical models) for speeding up the intensive post-processing + operations involved in our simulation-based decision making framework. + Scikit-learn's fit/predict API together with its efficient cross-validation + tools considerably eases the task of selecting the best-fit estimator. We are + also using scikit-learn for illustrating concepts in our training sessions. + Trainees are always impressed by the ease-of-use of scikit-learn despite the + apparent theoretical complexity of machine learning. -.. raw:: html + .. rst-class:: annotation - -

+ Vincent Dubourg, PHIMECA Engineering, PhD Engineer -.. image:: images/phimeca.png - :width: 120pt - :align: center - :target: https://www.phimeca.com/?lang=en + .. div:: image-box -.. raw:: html + .. image:: images/phimeca.png + :target: https://www.phimeca.com/?lang=en -

`HowAboutWe `_ ----------------------------------------------------------- - -.. raw:: html - -

- -At HowAboutWe, scikit-learn lets us implement a wide array of machine learning -techniques in analysis and in production, despite having a small team. We use -scikit-learn's classification algorithms to predict user behavior, enabling us -to (for example) estimate the value of leads from a given traffic source early -in the lead's tenure on our site. Also, our users' profiles consist of -primarily unstructured data (answers to open-ended questions), so we use -scikit-learn's feature extraction and dimensionality reduction tools to -translate these unstructured data into inputs for our matchmaking system. - -.. raw:: html +------------------------------------------ - +.. div:: sk-text-image-grid-large -Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe + .. div:: text-box -.. raw:: html + At HowAboutWe, scikit-learn lets us implement a wide array of machine learning + techniques in analysis and in production, despite having a small team. We use + scikit-learn's classification algorithms to predict user behavior, enabling us + to (for example) estimate the value of leads from a given traffic source early + in the lead's tenure on our site. Also, our users' profiles consist of + primarily unstructured data (answers to open-ended questions), so we use + scikit-learn's feature extraction and dimensionality reduction tools to + translate these unstructured data into inputs for our matchmaking system. - -

+ .. rst-class:: annotation -.. image:: images/howaboutwe.png - :width: 120pt - :align: center - :target: http://www.howaboutwe.com/ + Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe -.. raw:: html + .. div:: image-box -

+ .. image:: images/howaboutwe.png + :target: http://www.howaboutwe.com/ `PeerIndex `_ ------------------------------------------------------------------ -.. raw:: html - -

- -At PeerIndex we use scientific methodology to build the Influence Graph - a -unique dataset that allows us to identify who's really influential and in which -context. To do this, we have to tackle a range of machine learning and -predictive modeling problems. Scikit-learn has emerged as our primary tool for -developing prototypes and making quick progress. From predicting missing data -and classifying tweets to clustering communities of social media users, scikit- -learn proved useful in a variety of applications. Its very intuitive interface -and excellent compatibility with other python tools makes it and indispensable -tool in our daily research efforts. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + At PeerIndex we use scientific methodology to build the Influence Graph - a + unique dataset that allows us to identify who's really influential and in which + context. To do this, we have to tackle a range of machine learning and + predictive modeling problems. Scikit-learn has emerged as our primary tool for + developing prototypes and making quick progress. From predicting missing data + and classifying tweets to clustering communities of social media users, scikit- + learn proved useful in a variety of applications. Its very intuitive interface + and excellent compatibility with other python tools makes it and indispensable + tool in our daily research efforts. -Ferenc Huszar - Senior Data Scientist at Peerindex + .. rst-class:: annotation -.. raw:: html + Ferenc Huszar, Senior Data Scientist at Peerindex - -

+ .. div:: image-box -.. image:: images/peerindex.png - :width: 120pt - :align: center - :target: https://www.brandwatch.com/peerindex-and-brandwatch - -.. raw:: html - -

+ .. image:: images/peerindex.png + :target: https://www.brandwatch.com/peerindex-and-brandwatch `DataRobot `_ ---------------------------------------- -.. raw:: html - -

- -DataRobot is building next generation predictive analytics software to make data scientists more productive, and scikit-learn is an integral part of our system. The variety of machine learning techniques in combination with the solid implementations that scikit-learn offers makes it a one-stop-shopping library for machine learning in Python. Moreover, its consistent API, well-tested code and permissive licensing allow us to use it in a production environment. Scikit-learn has literally saved us years of work we would have had to do ourselves to bring our product to market. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Jeremy Achin, CEO & Co-founder DataRobot Inc. + DataRobot is building next generation predictive analytics software to make data + scientists more productive, and scikit-learn is an integral part of our system. The + variety of machine learning techniques in combination with the solid implementations + that scikit-learn offers makes it a one-stop-shopping library for machine learning + in Python. Moreover, its consistent API, well-tested code and permissive licensing + allow us to use it in a production environment. Scikit-learn has literally saved us + years of work we would have had to do ourselves to bring our product to market. -.. raw:: html + .. rst-class:: annotation - -

+ Jeremy Achin, CEO & Co-founder DataRobot Inc. -.. image:: images/datarobot.png - :width: 120pt - :align: center - :target: https://www.datarobot.com + .. div:: image-box -.. raw:: html - -

+ .. image:: images/datarobot.png + :target: https://www.datarobot.com `OkCupid `_ --------------------------------------- - -.. raw:: html - -

+------------------------------------- -We're using scikit-learn at OkCupid to evaluate and improve our matchmaking -system. The range of features it has, especially preprocessing utilities, means -we can use it for a wide variety of projects, and it's performant enough to -handle the volume of data that we need to sort through. The documentation is -really thorough, as well, which makes the library quite easy to use. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + We're using scikit-learn at OkCupid to evaluate and improve our matchmaking + system. The range of features it has, especially preprocessing utilities, means + we can use it for a wide variety of projects, and it's performant enough to + handle the volume of data that we need to sort through. The documentation is + really thorough, as well, which makes the library quite easy to use. -David Koh - Senior Data Scientist at OkCupid + .. rst-class:: annotation -.. raw:: html + David Koh - Senior Data Scientist at OkCupid - -

+ .. div:: image-box -.. image:: images/okcupid.png - :width: 120pt - :align: center - :target: https://www.okcupid.com - -.. raw:: html - -

+ .. image:: images/okcupid.png + :target: https://www.okcupid.com `Lovely `_ ----------------------------------- -.. raw:: html - -

- -At Lovely, we strive to deliver the best apartment marketplace, with respect to -our users and our listings. From understanding user behavior, improving data -quality, and detecting fraud, scikit-learn is a regular tool for gathering -insights, predictive modeling and improving our product. The easy-to-read -documentation and intuitive architecture of the API makes machine learning both -explorable and accessible to a wide range of python developers. I'm constantly -recommending that more developers and scientists try scikit-learn. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Simon Frid - Data Scientist, Lead at Lovely + At Lovely, we strive to deliver the best apartment marketplace, with respect to + our users and our listings. From understanding user behavior, improving data + quality, and detecting fraud, scikit-learn is a regular tool for gathering + insights, predictive modeling and improving our product. The easy-to-read + documentation and intuitive architecture of the API makes machine learning both + explorable and accessible to a wide range of python developers. I'm constantly + recommending that more developers and scientists try scikit-learn. -.. raw:: html + .. rst-class:: annotation - -

+ Simon Frid - Data Scientist, Lead at Lovely -.. image:: images/lovely.png - :width: 120pt - :align: center - :target: https://livelovely.com - -.. raw:: html - -

+ .. div:: image-box + .. image:: images/lovely.png + :target: https://livelovely.com `Data Publica `_ ---------------------------------------------- -.. raw:: html - -

- -Data Publica builds a new predictive sales tool for commercial and marketing teams called C-Radar. -We extensively use scikit-learn to build segmentations of customers through clustering, and to predict future customers based on past partnerships success or failure. -We also categorize companies using their website communication thanks to scikit-learn and its machine learning algorithm implementations. -Eventually, machine learning makes it possible to detect weak signals that traditional tools cannot see. -All these complex tasks are performed in an easy and straightforward way thanks to the great quality of the scikit-learn framework. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + Data Publica builds a new predictive sales tool for commercial and marketing teams + called C-Radar. We extensively use scikit-learn to build segmentations of customers + through clustering, and to predict future customers based on past partnerships + success or failure. We also categorize companies using their website communication + thanks to scikit-learn and its machine learning algorithm implementations. + Eventually, machine learning makes it possible to detect weak signals that + traditional tools cannot see. All these complex tasks are performed in an easy and + straightforward way thanks to the great quality of the scikit-learn framework. -Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica + .. rst-class:: annotation -.. raw:: html + Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica - -

- -.. image:: images/datapublica.png - :width: 120pt - :align: center - :target: http://www.data-publica.com/ - -.. raw:: html - -

+ .. div:: image-box + .. image:: images/datapublica.png + :target: http://www.data-publica.com/ `Machinalis `_ ------------------------------------------- -.. raw:: html - -

+.. div:: sk-text-image-grid-large -Scikit-learn is the cornerstone of all the machine learning projects carried at -Machinalis. It has a consistent API, a wide selection of algorithms and lots -of auxiliary tools to deal with the boilerplate. -We have used it in production environments on a variety of projects -including click-through rate prediction, `information extraction `_, -and even counting sheep! + .. div:: text-box -In fact, we use it so much that we've started to freeze our common use cases -into Python packages, some of them open-sourced, like -`FeatureForge `_ . -Scikit-learn in one word: Awesome. + Scikit-learn is the cornerstone of all the machine learning projects carried at + Machinalis. It has a consistent API, a wide selection of algorithms and lots of + auxiliary tools to deal with the boilerplate. We have used it in production + environments on a variety of projects including click-through rate prediction, + `information extraction `_, and even counting + sheep! -.. raw:: html + In fact, we use it so much that we've started to freeze our common use cases + into Python packages, some of them open-sourced, like `FeatureForge + `_. Scikit-learn in one word: Awesome. - + .. rst-class:: annotation -Rafael Carrascosa, Lead developer + Rafael Carrascosa, Lead developer -.. raw:: html + .. div:: image-box - -

- -.. image:: images/machinalis.png - :width: 120pt - :align: center - :target: https://www.machinalis.com/ - -.. raw:: html - -

+ .. image:: images/machinalis.png + :target: https://www.machinalis.com/ `solido `_ ----------------------------------------- -.. raw:: html - -

+.. div:: sk-text-image-grid-large -Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates -computer-aided design tools used by the majority of top-20 semiconductor -companies and fabs, to design the bleeding-edge chips inside smartphones, -automobiles, and more. Scikit-learn helps to power Solido's algorithms for -rare-event estimation, worst-case verification, optimization, and more. At -Solido, we are particularly fond of scikit-learn's libraries for Gaussian -Process models, large-scale regularized linear regression, and classification. -Scikit-learn has increased our productivity, because for many ML problems we no -longer need to “roll our own” code. `This PyData 2014 talk `_ has details. + .. div:: text-box + Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates + computer-aided design tools used by the majority of top-20 semiconductor + companies and fabs, to design the bleeding-edge chips inside smartphones, + automobiles, and more. Scikit-learn helps to power Solido's algorithms for + rare-event estimation, worst-case verification, optimization, and more. At + Solido, we are particularly fond of scikit-learn's libraries for Gaussian + Process models, large-scale regularized linear regression, and classification. + Scikit-learn has increased our productivity, because for many ML problems we no + longer need to “roll our own” code. `This PyData 2014 talk + `_ has details. -.. raw:: html + .. rst-class:: annotation - + Trent McConaghy, founder, Solido Design Automation Inc. -Trent McConaghy, founder, Solido Design Automation Inc. - -.. raw:: html - - -

- -.. image:: images/solido_logo.png - :width: 120pt - :align: center - :target: https://www.solidodesign.com/ - -.. raw:: html - -

+ .. div:: image-box + .. image:: images/solido_logo.png + :target: https://www.solidodesign.com/ `INFONEA `_ ------------------------------------------ - -.. raw:: html - -

- -We employ scikit-learn for rapid prototyping and custom-made Data Science -solutions within our in-memory based Business Intelligence Software -INFONEA®. As a well-documented and comprehensive collection of -state-of-the-art algorithms and pipelining methods, scikit-learn enables -us to provide flexible and scalable scientific analysis solutions. Thus, -scikit-learn is immensely valuable in realizing a powerful integration of -Data Science technology within self-service business analytics. +--------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Thorsten Kranz, Data Scientist, Coma Soft AG. + We employ scikit-learn for rapid prototyping and custom-made Data Science + solutions within our in-memory based Business Intelligence Software + INFONEA®. As a well-documented and comprehensive collection of + state-of-the-art algorithms and pipelining methods, scikit-learn enables + us to provide flexible and scalable scientific analysis solutions. Thus, + scikit-learn is immensely valuable in realizing a powerful integration of + Data Science technology within self-service business analytics. -.. raw:: html + .. rst-class:: annotation - -

+ Thorsten Kranz, Data Scientist, Coma Soft AG. -.. image:: images/infonea.jpg - :width: 120pt - :align: center - :target: http://www.infonea.com/en/ + .. div:: image-box -.. raw:: html - -

+ .. image:: images/infonea.jpg + :target: http://www.infonea.com/en/ `Dataiku `_ ------------------------------------------ - -.. raw:: html - -

+------------------------------------- -Our software, Data Science Studio (DSS), enables users to create data services -that combine `ETL `_ with -Machine Learning. Our Machine Learning module integrates -many scikit-learn algorithms. The scikit-learn library is a perfect integration -with DSS because it offers algorithms for virtually all business cases. Our goal -is to offer a transparent and flexible tool that makes it easier to optimize -time consuming aspects of building a data service, preparing data, and training -machine learning algorithms on all types of data. +.. div:: sk-text-image-grid-large + .. div:: text-box -.. raw:: html + Our software, Data Science Studio (DSS), enables users to create data services + that combine `ETL `_ with + Machine Learning. Our Machine Learning module integrates + many scikit-learn algorithms. The scikit-learn library is a perfect integration + with DSS because it offers algorithms for virtually all business cases. Our goal + is to offer a transparent and flexible tool that makes it easier to optimize + time consuming aspects of building a data service, preparing data, and training + machine learning algorithms on all types of data. - + .. rst-class:: annotation -Florian Douetteau, CEO, Dataiku + Florian Douetteau, CEO, Dataiku -.. raw:: html + .. div:: image-box - -

+ .. image:: images/dataiku_logo.png + :target: https://www.dataiku.com/ -.. image:: images/dataiku_logo.png - :width: 120pt - :align: center - :target: https://www.dataiku.com/ - -.. raw:: html - -

`Otto Group `_ ------------------------------------------ - -.. raw:: html - -

- -Here at Otto Group, one of global Big Five B2C online retailers, we are using -scikit-learn in all aspects of our daily work from data exploration to development -of machine learning application to the productive deployment of those services. -It helps us to tackle machine learning problems ranging from e-commerce to logistics. -It consistent APIs enabled us to build the `Palladium REST-API framework -`_ around it and continuously deliver -scikit-learn based services. - +-------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Christian Rammig, Head of Data Science, Otto Group + Here at Otto Group, one of global Big Five B2C online retailers, we are using + scikit-learn in all aspects of our daily work from data exploration to development + of machine learning application to the productive deployment of those services. + It helps us to tackle machine learning problems ranging from e-commerce to logistics. + It consistent APIs enabled us to build the `Palladium REST-API framework + `_ around it and continuously deliver + scikit-learn based services. -.. raw:: html + .. rst-class:: annotation - -

+ Christian Rammig, Head of Data Science, Otto Group -.. image:: images/ottogroup_logo.png - :width: 120pt - :align: center - :target: https://ottogroup.com + .. div:: image-box -.. raw:: html + .. image:: images/ottogroup_logo.png + :target: https://ottogroup.com -

`Zopa `_ ------------------------------------------ +--------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -

+ .. div:: text-box -At Zopa, the first ever Peer-to-Peer lending platform, we extensively use scikit-learn -to run the business and optimize our users' experience. It powers our -Machine Learning models involved in credit risk, fraud risk, marketing, and pricing, -and has been used for originating at least 1 billion GBP worth of Zopa loans. -It is very well documented, powerful, and simple to use. We are grateful for the -capabilities it has provided, and for allowing us to deliver on our mission of making -money simple and fair. + At Zopa, the first ever Peer-to-Peer lending platform, we extensively use + scikit-learn to run the business and optimize our users' experience. It powers our + Machine Learning models involved in credit risk, fraud risk, marketing, and pricing, + and has been used for originating at least 1 billion GBP worth of Zopa loans. It is + very well documented, powerful, and simple to use. We are grateful for the + capabilities it has provided, and for allowing us to deliver on our mission of + making money simple and fair. -.. raw:: html + .. rst-class:: annotation - + Vlasios Vasileiou, Head of Data Science, Zopa -Vlasios Vasileiou, Head of Data Science, Zopa + .. div:: image-box -.. raw:: html + .. image:: images/zopa.png + :target: https://zopa.com - -

- -.. image:: images/zopa.png - :width: 120pt - :align: center - :target: https://zopa.com - -.. raw:: html - -

`MARS `_ --------------------------------------- +------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -

+ .. div:: text-box -Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether -we're designing better recipes for petfood or closely analysing our cocoa -supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas -and taking them to production. This allows us to better understand and meet -the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is -easy to use and equips our associates with the capabilities they need to -solve the business challenges they face every day. + Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether + we're designing better recipes for petfood or closely analysing our cocoa + supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas + and taking them to production. This allows us to better understand and meet + the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is + easy to use and equips our associates with the capabilities they need to + solve the business challenges they face every day. -.. raw:: html + .. rst-class:: annotation - + Michael Fitzke, Next Generation Technologies Sr Leader, Mars Inc. -Michael Fitzke Next Generation Technologies Sr Leader, Mars Inc. + .. div:: image-box -.. raw:: html - - -

- -.. image:: images/mars.png - :width: 120pt - :align: center - :target: https://www.mars.com/global - -.. raw:: html - -

+ .. image:: images/mars.png + :target: https://www.mars.com/global `BNP Paribas Cardif `_ --------------------------------------------------------- -.. raw:: html - -

- -BNP Paribas Cardif uses scikit-learn for several of its machine learning models -in production. Our internal community of developers and data scientists has -been using scikit-learn since 2015, for several reasons: the quality of the -developments, documentation and contribution governance, and the sheer size of -the contributing community. We even explicitly mention the use of -scikit-learn's pipelines in our internal model risk governance as one of our -good practices to decrease operational risks and overfitting risk. As a way to -support open source software development and in particular scikit-learn -project, we decided to participate to scikit-learn's consortium at La Fondation -Inria since its creation in 2018. - -.. raw:: html - - +.. div:: sk-text-image-grid-large -Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif + .. div:: text-box -.. raw:: html + BNP Paribas Cardif uses scikit-learn for several of its machine learning models + in production. Our internal community of developers and data scientists has + been using scikit-learn since 2015, for several reasons: the quality of the + developments, documentation and contribution governance, and the sheer size of + the contributing community. We even explicitly mention the use of + scikit-learn's pipelines in our internal model risk governance as one of our + good practices to decrease operational risks and overfitting risk. As a way to + support open source software development and in particular scikit-learn + project, we decided to participate to scikit-learn's consortium at La Fondation + Inria since its creation in 2018. - -

+ .. rst-class:: annotation -.. image:: images/bnp_paribas_cardif.png - :width: 120pt - :align: center - :target: https://www.bnpparibascardif.com/ + Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif -.. raw:: html + .. div:: image-box -

+ .. image:: images/bnp_paribas_cardif.png + :target: https://www.bnpparibascardif.com/