feast
feathr
Our great sponsors
| feast | feathr | |
|---|---|---|
| 8 | 9 | |
| 5,255 | 1,928 | |
| 1.9% | 1.2% | |
| 9.3 | 6.7 | |
| 4 days ago | 24 days ago | |
| Python | Scala | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
feast
- What's Happening with Feast?
-
Running The Feast Feature Store With Dragonfly
Feast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.
-
Ask HN: How to Break into AI Engineering
AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI).
There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.
So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.
[0] https://github.com/feast-dev/feast
-
In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
A feature store usually stores features which are used for training ML model. It is a centralized place for collaboration between data engineer, ML engineer, and data scientist, so that data engineer can write to the feature store while ML engineer and data scientist read from it. Hopsworks https://www.hopsworks.ai and feast https://github.com/feast-dev/feast are examples of open source feature store.
- [D] Your 🫵 Preferred Feature Stores?
-
[P] Announcing Feast 0.10: The simplest way to serve features in production
Github: https://github.com/feast-dev/feast
-
[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
Have you looked at Feats as a Feature Store solution? It seems promising but I haven't really looked into it yet though.
- Feast: OSS Feature Store for Production ML
feathr
-
[D] good feature store?
For open source/free feature stores, look into Feathr https://github.com/feathr-ai/feathr and Feast https://feast.dev/.
- Open sourcing Feathr – LinkedIn’s feature store for productive machine learning
- Show HN: Feathr – An Open-Source, Enterprise-Grade Virtual Feature Store
-
[P] Feathr - An Open-Source, Enterprise-Grade and High-Performance Feature Store
Open Sourcing Feathr
- [D] Your 🫵 Preferred Feature Stores?
- Feathr – LinkedIn Open Sourced Its Feature Store
- Feathr – an enterprise-grade, high performance feature store
-
LinkedIn Open-Sources ‘Feathr’, It’s Feature Store To Simplify Machine Learning (ML) Feature Management And Improve Developer Productivity
LinkedIn research team has recently open-sourced feature store, Feathr, created to simplify machine learning (ML) feature management and increase developer productivity. Feathr is used by dozens of LinkedIn applications to define features, compute them for training, deploy them in production, and share them across consumers. Compared to previous application-specific feature pipeline solutions, Feathr users reported significantly reduced time required to add new features to model training and improved runtime performance.
What are some alternatives?
kedro-great - The easiest way to integrate Kedro and Great Expectations
hopsworks - Hopsworks - Data-Intensive AI platform with a Feature Store
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Milvus - A cloud-native vector database, storage for next generation AI applications
OpenMLDB - OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
metarank - A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
great_expectations - Always know what to expect from your data.
Clustering4Ever - C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
CIlib - Typesafe, purely functional Computational Intelligence