bytehub
OpenMLDB
Our great sponsors
| bytehub | OpenMLDB | |
|---|---|---|
| 3 | 9 | |
| 57 | 1,550 | |
| - | 2.3% | |
| 0.0 | 9.6 | |
| almost 3 years ago | 6 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 only | 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.
bytehub
- [D] Your 🫵 Preferred Feature Stores?
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ByteHub: simple timeseries data preparation in Python
Hi everyone! We’ve been building a Python-based feature-store called ByteHub. The aim is to make time series data easy to store, access, and transform when building machine-learning models. It’s available as an open-source library or as a low-cost cloud-hosted service.
- Show HN: Easy-to-use feature store for ML
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
What are some alternatives?
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
Open3D - Open3D: A Modern Library for 3D Data Processing
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
psychec - A compiler frontend for the C programming language
covalent - Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
libpmemobj-cpp - C++ bindings & containers for libpmemobj
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
feast - Feature Store for Machine Learning
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.