Scikit-learn
some perspectives
Lundi 17 septembre 2018 Lancement de l’initjatjve scikit-learn @ Fondatjon Inria
Scikit-learn some perspectives Lundi 17 septembre 2018 Lancement - - PowerPoint PPT Presentation
Lundi 17 septembre 2018 Lancement de linitjatjve scikit-learn @ Fondatjon Inria Scikit-learn some perspectives Lundi 17 septembre 2018 Lancement de linitjatjve scikit-learn @ Fondatjon Inria Development dynamics & prospects Lundi
Lundi 17 septembre 2018 Lancement de l’initjatjve scikit-learn @ Fondatjon Inria
Lundi 17 septembre 2018 Lancement de l’initjatjve scikit-learn @ Fondatjon Inria
Lundi 17 septembre 2018 Lancement de l’initjatjve scikit-learn @ Fondatjon Inria
Scikit-learn : the vision
Mathematjcal building blocks of AI Accessible beyond mathematjcians and computer scientjsts
Avoid oversimplifjcatjon : we target a technical audience Quality sofuware engineering
10 years
v0.1, released by researchers at Inria
2011
ODSC price for best tool
2010 v0.1 2011 v0.8 2012 v0.10 2013 v0.13 2014 v0.15 2015 v0.17 2016 v0.18 2017 v0.19 2018 v0.20 kNN Clustering Random Forest Modèles linéaires Détection d’anomalie Gradient Boosting Données sparses Parallélisme Sélection de modèle Calcul distribué Données catégorielles
Open Data Science Conference
Vitesse SVM
2009
1st internatjonal sprint
2017
10 years
Monthly website traffjc
A large impact
>500 000 actjve users 12 000 academic citatjons
50 30 20
Windows Mac Linux
34 63 3
Academic Industrial Other
Community-driven development
Monthly actjve contributeurs
Community-driven development
Contributor’s actjvity Actjvity Contributor
A few very actjve people Many occasional contributors
A professional core
Actjvity Contributor
stagiaires
Paid to work on the project (2018): Public research money
Scikit-learn @ fondation Inria
Keep the energy and the confjdence of the community
Take strategic recommendatjons from the industry
Sponsoring Mix community-driven development with industrial interest
Lundi 17 septembre 2018 Lancement de l’initjatjve scikit-learn @ Fondatjon Inria
Positioning
Databases Polyvalence Computational performance Model complexity Large- scale
Scikit-learn, new ambitions
Scaling up
Data integratjon
Interpretability & understanding
Technical committee
More frequent versions
Thanks to our partners
Alexandre Gramfort Alexander Fabisch Alexandre Passos Andreas Mueller Arnaud Joly Brian Holt Bertrand Thirion David Cournapeau David Warde-Farley Fabian Pedregosa Gael Varoquaux Guillaume Lemaitre Gilles Louppe Jake Vanderplas Jaques Grobler Jan Hendrik Metzen Jacob Schreiber Joel Nothman Joris Van den Bossche Kyle Kastner Lars Buitjnck Loïc Estève Shiqiao Du Mathieu Blondel Manoj Kumar Noel Dawe Nelle Varoquaux Olivier Grisel Paolo Losi Peter Pretuenhofer Hanmin Qin Raghav Rajagopalan Robert Layton Ron Weiss Roman Yurchak Tom Dupré la Tour Vlad Niculae Vincent Michel Wei Li And many others