MLx Cases 2026: End-to-End AI System Building (Online, May)

[Oxford Machine Learning (OxML)]

MLx Cases 2026

Online | 7–9 & 15–16 May

MLx Cases is the most practical module of OxML, designed for engineers, researchers, and builders who want to move beyond models and notebooks to owning working AI systems.

Across five intensive days, participants build real systems from the ground up, guided by experts working at the frontier of modern AI:

Hands-on case studies include:

• Building a MiniGPT
  Sizhe Yuen — The Alan Turing Institute

• Building diffusion models for images & sequences
  Jialin Yu — University of Oxford

• Owning your AI agent: running open-source agents on your terms
  Davide Eynard & David de la Iglesia Castro — Mozilla AI

• Building a CNN for image classification
  Noor Sajid — Harvard University

• Building AI agents for scheduling & travel planning
  Eric Wang — UC Santa Barbara / Simular AI

What distinguishes MLx Cases:

• End-to-end ML engineering and AI system development

• Hands-on case studies spanning LLMs, diffusion models, and AI agents

• System-level thinking: data pipelines, APIs, evaluation, deployment, and failure modes

The module is designed for participants who want to build, run, and deploy real AI systems, with guided instruction throughout.

Registration deadline: 7 April 2026

Places are limited to preserve a hands-on format.

Programme details and registration:

https://www.oxfordml.school/2026