Cross-platform, customizable ML solutions for live and streaming media.
- mediapipe
- c-plus-plus
- computer-vision
- deep-learning
47,133 repositories indexed
1–19 of 19 · Most stars
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
Tenzir is the data pipeline engine for security teams.
The repository host gives no description.
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.