758 matching collectives
SOUL
About
Where Founders build their narrative.
Koncierge • E-commerce & Shopify Specialists
About
Sculpter votre e-commerce d'exception : stratégie, design & développement 💎
BEAURECIT
About
→ Book online : https://shorturl.at/gkcHv
Infodroid
About
Programmé pour l'innovation, conçu pour l'avenir informatique ! ©ChatGPT
Brunch ☕
About
Growth & Brand
Hyperstack
About
Data team as a Service
Blocs
About
Experts en construction d'espace Notion & knowledge management
Fonction Labs
About
Building the future of data for you
Brick Par Brick
About
Le collectif pour e-commerçant au service de votre croissance
Officehour
About
Collectif de 3 développeurs fullstack (IA, React, Go, PHP, Mobile)
Half Square
About
Bureau d'étude & Logiciel sur-mesure
OverSkill
About
OverSkill : le mouton à 12 pattes dont vous rêvez
Link
About
Collectif expert dans le design, le marketing, et le développement, nous sommes spécialisés dans les MVPs et les refontes Web/Mobile
Café Crème
About
Donnez vie à vos idées : Design & développement
Vendredi Society
About
Digital Design Studio : Connecter les marques qui vont vite à des audiences en mouvement. La force créative d’équipes entièrement sur-mesure.
Yalla Cooperative
About
A flat-structure web design and development agency
Tekeo
About
Du concept au code, nous aidons les startups, TPE et PME à donner vie à leurs idées de produit
Qalitek
About
Une QA de qualité au service de la tech
BRED
•Financial Services•Enterprise
Machine Learning automation at BRED
Implementing big data pipelines and deploying machine learning models to predict and analyze customer behavior for a commercial bank.
Achievements:
• Big Data Pipeline Implementation: Setting up necessary infrastructures to collect and process data.
• Variable Processing and Aggregation: Using Spark and SQL queries to clean data and generate new features.
• Developing machine learning models in Python to automate money laundering detection in bank accounts.
• Deployment of Machine Learning Prediction App on AWS with Docker / Kubernetes.
• Implementing Large Language Models: Utilizing Hugging Face and Tensorflow for analyzing customer calls.
Impact :
• Improved Money Laundering Detection: Our model automates detection twice as effectively as competitors' previous models.
• Continuous Alert Filtering: Deploying the application on AWS ensures continuous filtering of irrelevant money laundering alerts, reducing costs and workload for operational teams.
• Enhanced Textual Call Analysis: Language models greatly improve the analysis of customer call records, facilitating the categorization of explanations for unusual transactions.
Members
Expertises
From
Nov 2022 to Dec 2023