Émission de podcast sur le cloud computi

Le premier enseignement fondamental du cloud computing est simple mais essentiel : le cloud n’est pas une technologie unique, mais un modèle de fourniture de services informatiques à la demande. Cela signifie que, plutôt que d’acheter et de gérer des serveurs physiques, les entreprises accèdent à des ressources informatiques (stockage, puissance de calcul, bases de données, logiciels) via Internet, selon leurs besoins.

This podcast explores how individual graph nodes can be transformed into meaningful vector representations through knowledge graph embeddings. It discusses embedding techniques, representation learning, link prediction, semantic similarity, and how these methods enable machines to understand and reason over connected data. Perfect for data scientists, machine learning engineers, AI researchers, and graph practitioners working on next-generation intelligent systems.

This podcast explores how individual graph nodes are transformed into dense vector representations through knowledge graph embeddings, enabling machines to better understand relationships within connected data. It discusses representation learning, embedding techniques, similarity computation, link prediction, and how these methods power modern AI applications. Perfect for data scientists, machine learning engineers, AI researchers, and graph practitioners working on advanced graph-based intelligence systems.

This podcast explores the major updates and improvements introduced in Neo4j Java Driver 5.0 and how they enhance connectivity with graph databases. It discusses performance upgrades, API changes, session handling, asynchronous capabilities, and best practices for integrating Java applications with Neo4j. Perfect for Java developers, software engineers, and backend architects building scalable graph-powered applications.

This podcast explores how transformer-based language models and clustering techniques can be used to resolve keyword ambiguity and improve the quality of knowledge graphs. It discusses entity resolution, semantic similarity, text embeddings, unsupervised learning, and methods for transforming noisy data into structured, reliable knowledge. Perfect for AI engineers, NLP practitioners, data scientists, and knowledge graph professionals working with large-scale text and information systems.

This podcast explores the asynchronous Python driver for Neo4j and how it enables high-performance, non-blocking database interactions in modern applications.