AI engineers are great, but to scale it out to the organization you need an AI Platform team.

Similar to introducing Agile and DevOps companies have pilot projects to release their first genAI features. They would bring in people that have an affinity for both AI and applications together to form the first change agent in a company. Once you have a few teams, you notice that there is shared AI infrastructure, you need enablement and governance across. This pattern has been used to introduce Cloud, Security and Developer Experience.

In this talk we highlight:

• the shared components of the AI stack: proxies, caching, testing, feedback collection, guardrails, ...
• the steps (and struggles) to enable this across the whole engineering (hackathons, training, abstractions)
• how it fits in the existing SDLC workflow and processes (testing, versioning, observability, security)
• how we can leverage the knowledge of all platform teams together (CloudOps, SecOps, developer experience, data platform, and AI platform) for dealing with security, permissions, and performance