Build a Sovereign AI Model Factory: Create an ML Engineer Agent that evaluates, fine-tunes, deploys, and improves open models using Union.ai on your cloud.
YOU MUST RSVP ON LUMA: https://luma.com/uyo6mgp7
The best AI teams aren't renting every capability from an external API, they're building systems they own and control. In this hands-on session at the AWS Loft, we'll build exactly that.
We'll start with a walkthrough where we build a LangGraph-powered agent that evaluates open models in parallel, fine-tunes promising candidates, deploys the winner, and retrains automatically when performance drops.
Think of it as the first brick in your own model factory: a repeatable system for evaluating, improving, and deploying models you control, not a black box you call and hope for the best.
Along the way, you'll see how Union.ai gives your workflows a durable runtime with infrastructure as context, so agents adapt infra resources, recover from failures, and keep moving without re-running completed work. And because it all runs in your own AWS account, you own the full stack end to end: your models, your data, your infrastructure, your IP.
Then we switch into hack night mode: keep building on the example, bring your own models or datasets, experiment with evals and fine-tuning, extend open models to different parts of your agent, or start something new around the same ideas.
For teams thinking about sovereign AI, this is a practical starting point for owning more of your stack, the weights, the training loop, the evals, and the infrastructure it runs on, all on AWS.
Bring a laptop. Leave with a model factory.
