Cross-platform, customizable ML solutions for live and streaming media.
- mediapipe
- c-plus-plus
- computer-vision
- deep-learning
47,133 repositories indexed
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Cross-platform, customizable ML solutions for live and streaming media.
Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
ZeroMQ core engine in C++, implements ZMTP/3.1
A modern high-performance open source message queuing system
The fastest unified streaming SQL Engine in a single C++ binary. ⚡ Millisecond latency. 100+ GB/s throughput. Continuously compute stateful context from streams, logs, metrics, events, and CDC.
Eclipse iceoryx™ - true zero-copy inter-process-communication
C++14 concurrent lock-free low-latency queue.
A RocksDB compliant high performance scalable embedded key-value store
iodine - HTTP / WebSockets Server for Ruby with Pub/Sub support
🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
Tenzir is the data pipeline engine for security teams.
oneAPI Data Analytics Library (oneDAL)
Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️
An implementation of Actor, Publish-Subscribe, and CSP models in one rather small C++ framework. With performance, quality, and stability proved by years in the production.
C++11 thread-safe container for sharing data between threads (synchronized queue)
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
Distributed C++ services from embedded to enterprise
An effort to bring Google's Firebase C++ API to Qt + QML
Daggy - Data Aggregation Utility and C/C++ developer library for data streams catching
Data management for the sensor-edge-cloud continuum
Multi-core Window-Based Stream Processing Engine
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.