bentoctl
hopsworks
Our great sponsors
| bentoctl | hopsworks | |
|---|---|---|
| 2 | 4 | |
| 175 | 1,071 | |
| - | 1.1% | |
| 4.3 | 9.3 | |
| about 2 months ago | 9 days ago | |
| Python | Java | |
| GNU General Public License v3.0 or later | 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.
bentoctl
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Show HN: Bentoctl – An open-source Terraform deployment tool for ML
Elastic License 2: https://github.com/bentoml/bentoctl/blob/v0.3.1/LICENSE.md which also applies to their Yatai kubernetes thing, but strangely not (yet?) to the similarly named repo which is Apache-2: https://github.com/bentoml/BentoML/blob/main/LICENSE
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?
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
gallery - BentoML Example Projects 🎨
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/
feast - Feature Store for Machine Learning
OpenMLDB - OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
iwlearn - "Production First" Machine Learning Framework
serverless-ml-course - Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features
bytehub - ByteHub: making feature stores simple
Milvus - A cloud-native vector database, storage for next generation AI applications
business-card-scan - Virtual Business Card Swapping with .NET MAUI + Azure Cognitive Services Form Recognizer