The Open Source
Innovation Spring 2019

Qualité logicielle | IoT | Cloud | Big Data | Systèmes d'information

Mars-juin 2019, Paris - Châtillon

Présidence : Roberto Di Cosmo, vice-président du GTLL, directeur de l’Irill, directeur de recherche Inria, Professeur à l’Université Paris-Diderot

Organisé par : logo Systematic & logo IRILL

12 Juin 2017 — PyParis


ESILV, Paris "Big Data"


The PyParis conference (June 12-13 2017) is a gathering of users and developers of tools written in the Python programming language. Our goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply the language and tools to ever-evolving challenges in the vast realm of data analytics, cloud computing, web application development, IoT, scientific computing, etc. 

We aim to be an accessible, community-driven conference, with tutorials for novices, advanced topical workshops for practitioners, and opportunities for package developers and users to meet in person.

A major goal of PyParis is to provide a venue for users across all the various domains of information technology and computer science to share their experiences and their techniques, as well as highlight the triumphs and potential pitfalls of using Python for certain kinds of problems.

This year, we will have the following tracks:

  • Data analytics ("PyData") and scientific computing (including a special track dedicated to scikit-learn)
  • Apps and cloud computing
  • Core Python
  • Education

More information on the website.



Data 1

Data 2

Track Web / Cloud



Scikit Learn

Core / Web

  • Performant Python Burkhard Kloss

  • wolfcrypt: wrapping secrets in Python Moisés Guimarães de Medeiros

  • Unicode and bytes demystified Boris Feld


  • Program in Python against big data clusters from one VM, thanks to Docker Benjamin Guinebertière

  • Topic Modelling (and a lot more) with NLP framework Gensim Bhargav Srinivasa Desikan

  • Introduction to Data Analysis using Python Francis Wolinski

  • Data analysis with Pandas Joris Van den Bossche

  • Function-as-a-Service: A Pythonic Perspective on Serverless Computing Josef Spillner

  • An introduction to Deep Learning with mxnet Julien Simon

  • AsyncIO and aiohttp workshop Ludovic Gasc

Direction de programme

Programme dirigé par Stéfane Fermigier (Abilian), Gaël Varoquaux (Inria).

Programme résumé



Ville: Paris