feathr
hopsworks
Our great sponsors
feathr | hopsworks | |
---|---|---|
9 | 4 | |
1,928 | 1,074 | |
1.2% | 1.4% | |
6.7 | 9.2 | |
24 days ago | 5 days ago | |
Scala | Java | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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.
feathr
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[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
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[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
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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.
hopsworks
- Hopworks: MLOps platform with Python-centric Feature Store
- Show HN: Feature Store and Model Registry; Hopsworks 3.0
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[D] Your 🫵 Preferred Feature Stores?
Anyways -> https://github.com/logicalclocks/hopsworks
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Reflections on the Lack of Adoption of Domain Specific Languages [pdf]
We built the first open-source feature store for ML, https://github.com/logicalclocks/hopsworks , when every existing proprietary feature store (Uber Michelangelo and Bighead at AirBnb) were shouting about how their DSL for feature engineering was the future.
Fast-forward 2 years and it is clear that Data Scientists want to work with Python, not with a DSL. We based our Feature Store on a Dataframe API for Python/PySpark. The DSL can never evolve at the same rate as libraries in a general-purpose programming language. So, your DSL is great for show-casing a Feature Store, but when you need to compute embeddings or train a GAN or done any type of feature engineering that is not a simple time-window aggregation, you pull out Python (or Scala/Java). I am old enough to have seen many DSLs in different domains (GUIs, aspect-oriented programming, feature engineering) have their day in the sun only to be replaced by general-purpose programming languages due to their unmatched utility.
What are some alternatives?
feast - Feature Store for Machine Learning
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
textX - Domain-Specific Languages and parsers in Python made easy http://textx.github.io/textX/
OpenMLDB - OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
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
Clustering4Ever - C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.
iwlearn - "Production First" Machine Learning Framework
CIlib - Typesafe, purely functional Computational Intelligence
serverless-ml-course - Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features