About us
Welcome to the Building AI Together meetup!
💬 Join the community Slack group: https://slack.flyte.org/
Our community meetups are for data scientists and engineers in machine learning, infrastructure, and data. Our central topics are:
best practices for putting ml in production
ml and data workflow automation
machine learning at scale
data and machine learning pipelines
distributed computing
Kubernetes-native machine learning and data workflows
MLOps
This group is run by the wonderful people at Union.ai.
The founding team at Union created Flyte, the data-ware machine learning orchestrator.
Check Flyte out on GitHub ⭐: https://github.com/flyteorg/flyte
Flyte is a Kubernetes-native open-source platform for production-grade data and machine-learning pipelines. It caches executions, tracks data and dependencies, and integrates with countless data and ML stacks, including AWS Sagemaker, Distributed Tensorflow, PyTorch Distributed, Ray, AWS Batch, Kubernetes Pods, and more.
Union.ai also provides the open-source solutions Pandera for statistical validation and UnionML.
Upcoming events
1
Own Your AI: Build an Agentic AI Model Factory - Hack Night
San Francisco, San Francisco, CA, USA, CA, USBuild 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.
1 attendee
Past events
103
