753 lines
26 KiB
ReStructuredText
753 lines
26 KiB
ReStructuredText
:orphan:
|
|
|
|
.. title:: Testimonials
|
|
|
|
.. _testimonials:
|
|
|
|
==========================
|
|
Who is using scikit-learn?
|
|
==========================
|
|
|
|
`J.P.Morgan <https://www.jpmorgan.com>`_
|
|
----------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Stephen Simmons, VP, Athena Research, JPMorgan
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/jpmorgan.png
|
|
:target: https://www.jpmorgan.com
|
|
|
|
|
|
`Spotify <https://www.spotify.com>`_
|
|
------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/spotify.png
|
|
:target: https://www.spotify.com
|
|
|
|
|
|
`Inria <https://www.inria.fr/>`_
|
|
--------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
At INRIA, we use scikit-learn to support leading-edge basic research in many
|
|
teams: `Parietal <https://team.inria.fr/parietal/>`_ for neuroimaging, `Lear
|
|
<https://lear.inrialpes.fr/>`_ for computer vision, `Visages
|
|
<https://team.inria.fr/visages/>`_ for medical image analysis, `Privatics
|
|
<https://team.inria.fr/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
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/inria.png
|
|
:target: https://www.inria.fr/
|
|
|
|
|
|
`betaworks <https://betaworks.com>`_
|
|
------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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
|
|
<https://medium.com/i-data/the-digg-video-recommender-2f9ade7c4ba3>`_,
|
|
and Poncho's `dynamic heuristic subspace clustering
|
|
<https://medium.com/@DiggData/scaling-poncho-using-data-ca24569d56fd>`_.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Gilad Lotan, Chief Data Scientist
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/betaworks.png
|
|
:target: https://betaworks.com
|
|
|
|
|
|
`Hugging Face <https://huggingface.co>`_
|
|
----------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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 <https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983>`_
|
|
of our `NLP tasks <https://huggingface.co/coref/>`_, 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Julien Chaumond, Chief Technology Officer
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/huggingface.png
|
|
:target: https://huggingface.co
|
|
|
|
|
|
`Evernote <https://evernote.com>`_
|
|
----------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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 <http://blog.evernote.com/tech/2013/01/22/stay-classified/>`_
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Mark Ayzenshtat, VP, Augmented Intelligence
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/evernote.png
|
|
:target: https://evernote.com
|
|
|
|
|
|
`Télécom ParisTech <https://www.telecom-paristech.fr/>`_
|
|
--------------------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Alexandre Gramfort, Assistant Professor
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/telecomparistech.jpg
|
|
:target: https://www.telecom-paristech.fr/
|
|
|
|
|
|
`Booking.com <https://www.booking.com>`_
|
|
----------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Melanie Mueller, Data Scientist
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/booking.png
|
|
:target: https://www.booking.com
|
|
|
|
|
|
`AWeber <https://www.aweber.com/>`_
|
|
-----------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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.
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Michael Becker, Software Engineer, Data Analysis and Management Ninjas
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/aweber.png
|
|
:target: https://www.aweber.com
|
|
|
|
|
|
`Yhat <https://www.yhat.com>`_
|
|
------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Greg Lamp, Co-founder
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/yhat.png
|
|
:target: https://www.yhat.com
|
|
|
|
|
|
`Rangespan <http://www.rangespan.com>`_
|
|
---------------------------------------
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Jurgen Van Gael, Data Science Director
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/rangespan.png
|
|
:target: http://www.rangespan.com
|
|
|
|
|
|
`Birchbox <https://www.birchbox.com>`_
|
|
--------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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
|
|
|
|
Thierry Bertin-Mahieux, Data Scientist
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/birchbox.jpg
|
|
:target: https://www.birchbox.com
|
|
|
|
|
|
`Bestofmedia Group <http://www.bestofmedia.com>`_
|
|
-------------------------------------------------
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Eustache Diemert, Lead Scientist
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/bestofmedia-logo.png
|
|
:target: http://www.bestofmedia.com
|
|
|
|
|
|
`Change.org <https://www.change.org>`_
|
|
--------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/change-logo.png
|
|
:target: https://www.change.org
|
|
|
|
|
|
`PHIMECA Engineering <https://www.phimeca.com/?lang=en>`_
|
|
---------------------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Vincent Dubourg, PHIMECA Engineering, PhD Engineer
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/phimeca.png
|
|
:target: https://www.phimeca.com/?lang=en
|
|
|
|
|
|
`HowAboutWe <http://www.howaboutwe.com/>`_
|
|
------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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
|
|
|
|
Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/howaboutwe.png
|
|
:target: http://www.howaboutwe.com/
|
|
|
|
|
|
`PeerIndex <https://www.brandwatch.com/peerindex-and-brandwatch>`_
|
|
------------------------------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Ferenc Huszar, Senior Data Scientist at Peerindex
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/peerindex.png
|
|
:target: https://www.brandwatch.com/peerindex-and-brandwatch
|
|
|
|
|
|
`DataRobot <https://www.datarobot.com>`_
|
|
----------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Jeremy Achin, CEO & Co-founder DataRobot Inc.
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/datarobot.png
|
|
:target: https://www.datarobot.com
|
|
|
|
|
|
`OkCupid <https://www.okcupid.com/>`_
|
|
-------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
David Koh - Senior Data Scientist at OkCupid
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/okcupid.png
|
|
:target: https://www.okcupid.com
|
|
|
|
|
|
`Lovely <https://livelovely.com/>`_
|
|
-----------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Simon Frid - Data Scientist, Lead at Lovely
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/lovely.png
|
|
:target: https://livelovely.com
|
|
|
|
|
|
`Data Publica <http://www.data-publica.com/>`_
|
|
----------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/datapublica.png
|
|
:target: http://www.data-publica.com/
|
|
|
|
|
|
`Machinalis <https://www.machinalis.com/>`_
|
|
-------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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 <https://github.com/machinalis/iepy>`_, and even counting
|
|
sheep!
|
|
|
|
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
|
|
<https://github.com/machinalis/featureforge>`_. Scikit-learn in one word: Awesome.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Rafael Carrascosa, Lead developer
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/machinalis.png
|
|
:target: https://www.machinalis.com/
|
|
|
|
|
|
`solido <https://www.solidodesign.com/>`_
|
|
-----------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. 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
|
|
<https://www.youtube.com/watch?v=Jm-eBD9xR3w>`_ has details.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Trent McConaghy, founder, Solido Design Automation Inc.
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/solido_logo.png
|
|
:target: https://www.solidodesign.com/
|
|
|
|
|
|
`INFONEA <http://www.infonea.com/en/>`_
|
|
---------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Thorsten Kranz, Data Scientist, Coma Soft AG.
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/infonea.jpg
|
|
:target: http://www.infonea.com/en/
|
|
|
|
|
|
`Dataiku <https://www.dataiku.com/>`_
|
|
-------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
Our software, Data Science Studio (DSS), enables users to create data services
|
|
that combine `ETL <https://en.wikipedia.org/wiki/Extract,_transform,_load>`_ 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
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/dataiku_logo.png
|
|
:target: https://www.dataiku.com/
|
|
|
|
|
|
`Otto Group <https://ottogroup.com/>`_
|
|
--------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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
|
|
<https://github.com/ottogroup/palladium/>`_ around it and continuously deliver
|
|
scikit-learn based services.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Christian Rammig, Head of Data Science, Otto Group
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/ottogroup_logo.png
|
|
:target: https://ottogroup.com
|
|
|
|
|
|
`Zopa <https://zopa.com/>`_
|
|
---------------------------
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Vlasios Vasileiou, Head of Data Science, Zopa
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/zopa.png
|
|
:target: https://zopa.com
|
|
|
|
|
|
`MARS <https://www.mars.com/global>`_
|
|
-------------------------------------
|
|
|
|
.. 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.
|
|
|
|
.. rst-class:: annotation
|
|
|
|
Michael Fitzke, Next Generation Technologies Sr Leader, Mars Inc.
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/mars.png
|
|
:target: https://www.mars.com/global
|
|
|
|
|
|
`BNP Paribas Cardif <https://www.bnpparibascardif.com/>`_
|
|
---------------------------------------------------------
|
|
|
|
.. div:: sk-text-image-grid-large
|
|
|
|
.. div:: text-box
|
|
|
|
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
|
|
|
|
Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif
|
|
|
|
.. div:: image-box
|
|
|
|
.. image:: images/bnp_paribas_cardif.png
|
|
:target: https://www.bnpparibascardif.com/
|