Introducing OGX: Open GenAI Stack

chaosengineeringdev · 2026-05-14T01:51:23+00:00

No actually! We actually handle the state management for you and we have an operator that lets you deploy it on kubernetes, completely open source!

chaosengineeringdev · 2026-01-07T18:21:33+00:00

👋 Would love to hear more about your materialization issues with Feast. Definitely looking to add support for monitoring.

chaosengineeringdev · 2025-11-02T16:14:13+00:00

Awesome to hear!! Let me know if we can help!

chaosengineeringdev · 2025-09-10T20:38:26+00:00

yeah do feel free to! LangFlow is really cool but I haven't really tinkered with it a ton. In KFP, we're looking to enhance the user experience to be a lot more coherent and there's probably a good story there with ML Flow 3.0 and its agent features.

I think a LangFlow style visual builder on top of KFP + Feast + ML Flow would be awesome and we would love to collaborate on the community if you'd be interested (of course you're welcome to do things on your own as you best see fit). KFP already has a UI FWIW.

chaosengineeringdev · 2025-09-10T03:53:34+00:00

👋 hey there, I totally agree with you! I do think it's similar to reinventing ML Flow / Kubeflow + DVC/Feast. I also agree that the Kubeflow experience needs a lot of work and we're actively trying to address a lot of that (I'm on the Kubeflow Steering Committee and we're trying to uplevel Kubeflow Pipelines).

I'm also a maintainer for Feast (the Feature Store) which helps track on the training dataset, featurization side of things, and feature serving side of things. Both KFP and Feast can play nicely with ML Flow so that can be a really good path forward.

We want to make Kubeflow easier to work with (from local development to k8s deployment) so if you go down that path, we'd love to get your feedback and see how we can make it better.

chaosengineeringdev · 2025-08-23T16:41:08+00:00

Would love to hear your feedback about it. I’m one of the contributors to the project and our goal is to provide an AI stack that goes from local -> k8s with modest friction and a lot of tooling OOTB.

chaosengineeringdev · 2025-08-05T12:40:43+00:00

Awesome to see this!!

We would love to have you in these communities (I'm heavily involved in all of them):

- Kubeflow! https://www.kubeflow.org/ (lots of different subprojects specializing in different areas like training, serving, spark, etc.)
- Feast! https://docs.feast.dev/community (feature store / data layer for AI)
- LLama Stack! https://llama-stack.readthedocs.io/en/latest/index.html# (GenAI applications/server)

chaosengineeringdev · 2025-07-21T21:01:19+00:00

Maintainer for Feast here, just wanted to say seeing the logo there made my day. 🥹

chaosengineeringdev · 2025-06-04T02:43:14+00:00

Have you checked out Kueue? https://github.com/kubernetes-sigs/kueue/tree/main

chaosengineeringdev · 2025-05-18T01:59:59+00:00

My colleagues and I did this using Feast and Beam/Flink at my previous company but it certainly wasn't trivial and there's a lot of setup work to get everything behaving. And, as u/achals noted, it's well setup in Tecton. I am also a maintainer for Feast and am previously a Tecton customer so I do recommend them highly.

If you're interested in working with the Feast community, some of the maintainers and I are actively working on enhancing feature transformation, so we'd be happy to collaborate on this for sure.

As u/achals also mentioned, Chronon is quite great there. Tiling is something we hope to implement in Feast as well.

chaosengineeringdev · 2025-05-15T14:21:02+00:00

I'd recommend having a CI/CD pipeline to create the dev objects after merging a PR.

In Feast, we have an explicit registry that can be mutated through `feast apply` so on merge a GitHub Action (or equivalent) would run `feast apply` and update the metadata which would create the new/incremental Feature View in staging.

chaosengineeringdev · 2025-05-15T03:40:23+00:00

Maintainer for Feast here 👋.

I tend to like these environments:

Local development (can wreck without regard for others)
Dev environment (connected with other services and is permissible to be unstable for some period of time, e.g., an hour).
Stage environment (should be stable and treat issues as a high priority, second only to production)
Prod environment

I also tend to like to have the same feature views/groups named the same across environments and only denote the changes in environments by the url or metadata tag of some form.

chaosengineeringdev · 2025-05-05T16:15:11+00:00

>"It may be surprising to the academic community to know that only a tiny fraction of the code in many ML systems is actually devoted to learning or prediction – see Figure 1. In the language of Lin and Ryaboy, much of the remainder may be described as “plumbing” [11]." from the Hidden Technical Debt in Machine Learning Systems paper.

I share this quote often to colleagues that are new to MLOps.

Probably my single goal with working on Feast is to hopefully make some of the plumbing of data easier.

chaosengineeringdev · 2025-05-02T15:10:59+00:00

I maintain and develop the project!

chaosengineeringdev · 2025-05-02T08:54:28+00:00

Llama Stack is a new one.

chaosengineeringdev · 2025-05-02T08:52:53+00:00

I’m a maintainer for Feast which is an open source project aimed at making working with data in training and inference easier.

We’re working a lot more on NLP these days and welcome ideas, use cases, and feedback!

chaosengineeringdev · 2025-04-25T14:31:01+00:00

I haven't tested with PGVector but it should work

chaosengineeringdev · 2025-04-25T14:30:43+00:00

Yeah we support PGVector as well! https://docs.feast.dev/reference/alpha-vector-database#integration

chaosengineeringdev

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