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](https://www.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](https://www.union.ai/) also provides the open-source solutions Pandera for statistical validation and UnionML.
Upcoming events
1
ML for Fraud Detection with Feast and Flyte
·OnlineOnlineThe features your model trains on need to match the features it serves on, whether they're computed in a nightly batch job or fetched in real time at inference. A feature store closes that gap. In fintech, where a missed signal costs real money and a regulator will eventually ask exactly how your model produced a decision, that consistency isn't optional.
In this hands-on workshop, we'll build an end-to-end fraud detection pipeline with Feast as the feature store and Flyte 2 as the orchestrator. We'll engineer point-in-time correct features and train a model in a Flyte workflow, then deploy the model and Feast online store as a Union-hosted app for real-time predictions. Every input, output, and model is a versioned artifact with full lineage, and the same code scales from the workshop's local setup to production.
What we'll cover
- How feature stores keep batch training and real-time serving in sync
- Building point-in-time correct datasets with Feast
- Orchestrating training with Flyte 2: cached data prep, durable runs, and full lineage from source to model
- Deploying the model and feature store as a Union app for low-latency inference
- The path from demo to production: streaming ingest, drift monitoring, and scaled serving
What you'll leave with
- A working fraud detection model served behind a real-time API
- A reusable Flyte 2 pipeline you can adapt to your own data
- A portfolio-ready project you can adapt to a production scenario at work
Who it's for ML engineers and practitioners working on fraud, risk, or any production ML problem in a regulated environment.
Hosted by Sage Elliott, AI Engineer at Union.ai2 attendees
Past events
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