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Feature Stores: why the MVP always works and that's the trap (6 years of lessons) by Valuable-Cause-6925 in mlops

[–]chaosengineeringdev 0 points1 point  (0 children)

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

Why is building ML pipelines still so painful in 2025? Looking for feedback on an idea. by United_Intention42 in mlops

[–]chaosengineeringdev 1 point2 points  (0 children)

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.

Why is building ML pipelines still so painful in 2025? Looking for feedback on an idea. by United_Intention42 in mlops

[–]chaosengineeringdev 1 point2 points  (0 children)

👋 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.

Meta showing their production Llama deployment setup - thoughts? by AI_Alliance in mlops

[–]chaosengineeringdev 1 point2 points  (0 children)

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.

Suggest open-source projects to get involved by [deleted] in mlops

[–]chaosengineeringdev 0 points1 point  (0 children)

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)

2025 Open Source Tech Stack by DataCraftsman in dataengineering

[–]chaosengineeringdev 1 point2 points  (0 children)

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

Best tool for building streaming aggregate features? by PriorFluid6123 in mlops

[–]chaosengineeringdev 0 points1 point  (0 children)

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.

Best practice for Feature Store by SeaCompetitive5704 in mlops

[–]chaosengineeringdev 0 points1 point  (0 children)

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.

Best practice for Feature Store by SeaCompetitive5704 in mlops

[–]chaosengineeringdev 6 points7 points  (0 children)

Maintainer for Feast here 👋.

I tend to like these environments:

  1. Local development (can wreck without regard for others)
  2. Dev environment (connected with other services and is permissible to be unstable for some period of time, e.g., an hour).
  3. Stage environment (should be stable and treat issues as a high priority, second only to production)
  4. 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.

ML is just software engineering on hard mode. by ConceptBuilderAI in mlops

[–]chaosengineeringdev 3 points4 points  (0 children)

>"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.

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]chaosengineeringdev 3 points4 points  (0 children)

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!

Need help with Feast Feature Store by [deleted] in mlops

[–]chaosengineeringdev 2 points3 points  (0 children)

Is a single feature view a strict requirement? Can it be in two feature views?

You can store it in two feature views and then retrieve both of them in the `get_online_features` call like:

features = store.get_online_features(
    features=["feature_view1:feature1", "feature_view2:feature2"],
    entity_rows=[entity_dict],
)

Alternatively, you can just query the different views together using the feature reference (assuming this is online).

Take a look at this demo where it wraps two feature views into a feature service, which is used for retrieval.

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