CoSApp, for Collaborative System Approach, is a Python library dedicated to the simulation and design of multi-disciplinary systems. It is primarily intended for engineers and system architects during the early stage of industrial product design. The API of CoSApp is focused on simplicity and explicit declaration of design problems. Special attention is given to modularity; a very flexible mechanism of solver assembly allows users to construct complex, customized simulation workflows. This presentation aims at presenting the key features of the framework.

https://cosapp.readthedocs.io https://gitlab.com/cosapp/cosapp

Discover how North Data transforms raw data on European companies into actionable information in a clear, fast and efficient manner.

Beyond embarrassingly parallel processing problems, data must be shared between workers for them to do something useful. This can be done by: - sharing memory between threads, with the issue of preventing access to shared data to avoid race conditions. - copying memory to subprocesses, with the challenge of synchronizing data whenever it is mutated.

In Python, using threads is not an option because of the GIL (global interpreter lock), which prevents true parallelism. This might change in the future with the removal of the GIL, but usual problems with multithreading will appear, such as using locks and managing their complexity. Subprocesses don't suffer from the GIL, but usually need to access a database for sharing data, which is often too slow. Algorithms such as HAMT (hash array mapped trie) have been used to efficiently and safely share data stored in immutable data structures, removing the need for locks. In this talk we will show how CRDTs (conflict-free replicated data type) can be used for the same purpose.

Du Big Data à la vidéo instantanée : l’expérience client réinventée par PULP'IN Découvrez comment générer des expériences data driven à gran

Venez découvrir la seule solution de supervision fonctionnelle et de gouvernance des flux: Enterprise Flows Repository.

Comment utiliser l'IA pour évaluer la conformité d'une application à l'AI Act.

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing, operating and maintaining machine learning pipelines. We’ll break down these principles, examine their influence on pipeline architecture, and conclude with a straightforward, vendor-agnostic mind-map, offering a roadmap to build resilient MLOps systems for any project or technology stack. Despite the surge in tools and platforms, many teams still struggle with the same underlying issues: brittle data dependencies, poor observability, unclear ownership, and pipelines that silently break once deployed. Architecture alone isn't the answer — systems thinking is.

We'll use concrete examples to walk through common failure modes in ML pipelines, highlight where analogies fall apart, and show how to build systems that tolerate failure, adapt to change, and support iteration without regressions.

Topics covered include: - Common failure modes in ML pipelines - Modular design: feature, training, inference - Built-in observability, versioning, reuse - Orchestration across batch, real-time, LLMs - Platform-agnostic patterns that scale

Key takeaways: - Resilience > diagrams - Separate concerns, embrace change - Metadata is your backbone - Infra should support iteration, not block it

Discover how Insee transitioned from fastText to a PyTorch-based model for text classification by developing and open-sourcing the torchFastText package. This presentation will cover the creation, deployment, and practical applications of torchFastText in modernizing automatic coding systems, benefiting Insee and other European National Statistical Institutes (NSIs).

xeus-cpp is the next-generation Jupyter kernel for C++, replacing the outdated xeus-cling. It support recent versions of the language, comes with new features, can be extended and even provide a jupyter-lite kernel.

L'alliance entre un leader de l'assurance française et un pionnier de l'IA vocale souveraine.

Découvrez comment Risk Hunter vous permet de maîtriser votre GRC et cybersécurité grâce à notre plateforme AI Driven Innovante.

At CERN- the European Organization for Nuclear Research, machine learning is applied across a wide range of scenarios, from simulations and event reconstruction to classifying interesting experimental events, all while handling data rates in the order of terabytes per second. As a result, beyond developing complex models, CERN also requires highly optimized mechanisms for model inference.

From the ML4EP team at CERN, we have developed SOFIE (System for Optimized Fast Inference code Emit), an open-source tool designed for fast inference on ML models with minimal dependencies and low latency. SOFIE is under active development, driven by feedback not only from high-energy physics researchers but also from the broader scientific community.

With upcoming upgrades to CERN’s experiments expected to increase data generation, we have been investigating optimization methods to make SOFIE even more efficient in terms of time and memory usage, while improving its accessibility and ease of integration with other software stacks.

In this talk, we will introduce SOFIE and present novel optimization strategies developed to accelerate ML inference and reduce resource overhead.

jupyter-fs provides an interface between PyFilesystem and fsspec file systems, the JupyterLab user interface, and the Jupyter notebooks you run. Connect and browse your local filesystem, S3, Samba, WebDAV, and more, interacting with data seamlessly from both the JupyterLab UI and your notebook's kernel.

As AI floods the digital landscape with content, what happens when it starts repeating itself? This talk explores model collapse, a progressive erosion where LLMs and image generators loop on their own results, hindering the creation of novel output.

We will show how self-training leads to bias and loss of diversity, examine the causes of this degradation, and quantify its impact on model creativity. Finally, we will also present concrete strategies to safeguard the future of generative AI, emphasizing the critical need to preserve innovation and originality.

By the end of this talk, attendees will gain insights into the practical implications of model collapse, understanding its impact on content diversity and the long-term viability of AI.

Session de démo sur comment créer des agents IA en quelques clicks pour plusieurs cas d’usage : Compliance, IT, RH, support client, Ventes,

Plongez dans l’ère des opérations IT intelligentes où l’IA anticipe, décide et agit en temps réel.