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IA Générative pour la CDC et par Probayes : révolutionnez les réponses aux organismes de formation, optimisant temps et précision
Causal inference offers a principled way to estimate the effects of interventions—a critical need in industrial settings where decisions directly impact costs and performance. This talk presents a case study from Saint-Gobain, in collaboration with Inria, where we applied causal inference methods to production and quality data to reduce raw material usage without compromising product quality. We’ll walk through each step of a causal analysis: building a causal graph in collaboration with domain experts, identifying confounders, working with continuous treatments, and using open-source tools such as DoWhy, EconML, and DAGitty. The talk is aimed at data scientists with basic ML experience, looking to apply causal thinking to real-world, non-academic problems.
Processing documents with LLMs comes with unexpected challenges: handling long inputs, enforcing structured outputs, catching hallucinations, and recovering from partial failures. In this talk, we’ll cover why large context windows are not a silver bullet, why chunking is deceptively hard and how to design input and output that allow for intelligent retrial. We'll also share practical prompting strategies, discuss OCR and parsing tools, compare different LLMs (and their cloud APIs) and highlight real-world insights from our experience developing production GenAI applications with multiple document processing scenarios.
Deploying ML models doesn’t have to mean spinning up servers and writing backend code. This talk shows how to run machine learning inference directly in the browser—using ONNX and WebAssembly—to go from prototype to interactive demo in minutes, not weeks.
Découvrez comment GLS pilote plus d’1M de colis grâce à une Modern Data Stack et une BI Data Viz au service de la performance.
Concevoir la nouvelle application DATA ou IA que vous avez imaginée, parfaitement opérationnelle
Comment CENTRE FRANCE a mis en place un dataware virtuel capable d’agréger des données issues de multiples sources avec Denodo et Snowflake?
Bien menée, la gouvernance devient moteur : les Data Contracts (ODCS) rendent pipelines data/IA précis, fiables & conformes, sans blocages.
Et si nous parlions qualités - et non pas qualité - de la donnée ?
Comment éviter l’effet POC et faire de l’IA un vrai levier de performance ? Stratégie, méthode et retours d’expérience au programme 🚀
Changer d’échelle avec la data publique : une orga décentralisée & fédérée pour créer de la valeur partagée au cœur des ministères 💡
Observabilité des données pour une gouvernance moderne : avec Actian Data Intelligence, Data Products fiables et Data Contracts respectés.
nAIxt = plateforme de dév. et d'orchestration d'Agents IA d'ILLUIN, pour concevoir, déployer et surveiller des Agents IA, du POC à la Prod.
Every dataset has a story — and when it comes to geospatial data, it’s a story deeply rooted in space and scale. But working with geospatial information is often a hidden challenge: massive file sizes, strange formats, projections, and pipelines that don't scale easily.
In this talk, we'll follow the life of a real-world geospatial dataset, from its raw collection in the field to its transformation into meaningful insights. Along the way, we’ll uncover the key steps of building a robust, scalable open-source geospatial pipeline.
Drawing on years of experience at Camptocamp, we’ll explore:
This journey will show how the open-source ecosystem has matured to make geospatial big data accessible — and how spatial thinking can enrich almost any data project, whether you are building dashboards, doing analytics, or setting the stage for machine learning later on.
Comment les IA génératives réorientent les discussions économiques et législatives en Europe