OpenMLDB
hopsworks
Our great sponsors
OpenMLDB | hopsworks | |
---|---|---|
9 | 4 | |
1,550 | 1,074 | |
2.3% | 1.1% | |
9.6 | 9.2 | |
1 day ago | 2 days ago | |
C++ | 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.
OpenMLDB
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Comparative Analysis of Memory Consumption: OpenMLDB vs Redis Test Report
b. Pull the testing code
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Ultra High-Performance Database OpenM(ysq)LDB: Seamless Compatibility with MySQL Protocol and Multi-Language MySQL Client
OpenMLDB has introduced a new service module called OpenM(ysq)LDB, expanding its capabilities to integrate with MySQL infrastructure. This extension redefines the “ML” in OpenMLDB to signify both Machine Learning and MySQL compatibility. Through OpenM(ysq)LDB, users gain the ability to utilize MySQL command-line clients or MySQL SDKs in various programming languages, enabling seamless access to OpenMLDB’s unique online and offline feature calculation capabilities.
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Mastering Distributed Database Development in 10 Minutes with OpenMLDB Developer Docker Image
OpenMLDB is an open-source, distributed in-memory database system designed for time-series data. It focuses on high performance, reliability, and scalability, making it suitable for handling massive time-series data and real-time computation of online features. In the wave of big data and machine learning, OpenMLDB has emerged as a promising player in the open-source database field, thanks to its powerful data processing capabilities and efficient support for machine learning.
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OpenMLDB new release v0.8.4
For detailed release notes, please refer to: https://github.com/4paradigm/OpenMLDB/releases/tag/v0.8.4 Feel free to try it out, and discuss it in the official Slack channel (https://join.slack.com/t/openmldb/shared_invite/zt-ozu3llie-K~hn9Ss1GZcFW2~K_L5sMg) if you have any thoughts on improvements or questions!
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Quickstart with OpenMLDB
New to OpenMLDB? Check out the quick workflow and quickstart blog post!
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Engineering Practice for Real-time Feature Store in Decision-Making Machine Learning
Website: https://openmldb.ai/
- [D] Your 🫵 Preferred Feature Stores?
- OpenMLDB: An new open-source database for production AI/ML workloads
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?
Open3D - Open3D: A Modern Library for 3D Data Processing
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
psychec - A compiler frontend for the C programming language
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/
libpmemobj-cpp - C++ bindings & containers for libpmemobj
feast - Feature Store for Machine Learning
iwlearn - "Production First" Machine Learning Framework
serverless-ml-course - Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features