课程

Master the full lifecycle of AI product development, from problem validation and model selection to MLOps, monitoring, and cost optimisation.

Navigate the open-source AI landscape, from key platforms and models to running inference locally and building a complete AI stack.

Explore AGI timelines, global AI regulation, workforce transformation, breakthrough applications, existential risks, and your role in shaping what comes next.

Explore proven AI business models, niche selection, MVP strategy, funding, and competitive moats for building a successful AI startup.

Learn how to find projects, make meaningful contributions, write great PRs, and build a public profile through open-source AI work.

Learn the full MLOps lifecycle: model registries, CI/CD for ML, containerisation, serving infrastructure, drift monitoring, and cost optimisation at scale.

Explore model compression, on-device inference, and the hardware accelerators powering AI at the edge - from mobile phones to web browsers.

Navigate the global regulatory landscape for AI: the EU AI Act's risk framework, GDPR data rights, US executive orders, and compliance strategies for AI companies.

Understand the hardware and cloud infrastructure powering modern AI: GPU architectures, cloud platform comparisons, custom silicon, and inference optimisation.

Learn practical AI governance: frameworks, bias auditing, fairness metrics, explainability tools, ethics boards, incident response, and lessons from real-world failures.