https://www.lucavallin.com/it/blog/tags/ai Platform Engineer at Xebia, focused on AI platform engineering - the infrastructure behind reliable, observable, scalable AI and cloud-native workloads. I work primarily in Go and Google Cloud, with deep experience in Kubernetes, containers, and end-to-end observability - and a strong interest in networking and lower-level systems work in Rust. My current focus is the platform layer beneath AI: inference serving infrastructure on Kubernetes, AI gateway and MCP connectivity, agentic workload orchestration, and end-to-end observability for GenAI systems. My broader experience is full-stack: strong on backend, with solid frontend and mobile knowledge. I contribute to open source, write on my blog, and pick up the occasional talk, training, or meetup when something interesting comes up. I'm a Google Developer Expert (GDE) and a CNCF Ambassador. For a deeper dive, see my blog. If you're new to open source, check out Verto.sh. For mentorship, I'm on Mentorcruise. Outside of work, activities like photography, motorcycling, playing a handpan and cleaning litterboxes keep me occupied 🐈. it-IT Luca Cavallin Luca Cavallin Tue, 02 Jun 2026 00:00:00 GMT https://www.lucavallin.com/it/blog/ai-engineering-for-developers https://www.lucavallin.com/it/blog/ai-engineering-for-developers Un percorso attraverso l'AI engineering per sviluppatori che sanno già come mettere in produzione software. Quattordici capitoli, senza tono da LinkedIn, senza riscaldamento lento. Partiremo da 'cos'è un foundation model' fino a 'come si gestiscono agenti in produzione su Google Cloud' senza saltare le parti che contano. Tue, 02 Jun 2026 00:00:00 GMT Luca Cavallin ai https://www.lucavallin.com/it/blog/notes-i-took-while-reading-applied-machine-learning-and-ai-for-engineers-and-introducing-mlops https://www.lucavallin.com/it/blog/notes-i-took-while-reading-applied-machine-learning-and-ai-for-engineers-and-introducing-mlops Ho letto di recente i libri "Applied Machine Learning and AI for Engineers" e "Introducing MLOps", prendendo appunti per riassumere i concetti più importanti. In questo post condivido i miei spunti: dalle basi del supervised e unsupervised learning fino ad aree più avanzate come il deep learning e il natural language processing, passando per le idee fondamentali dietro MLOps. Thu, 18 Jul 2024 00:00:00 GMT Luca Cavallin ai