Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
Die Hauptfunktionen von oryxproject/oryx sind: Machine Learning, Machine Learning and Analytics, Machine-Learning-Frameworks, Recommender Frameworks, Big Data Frameworks.
Open-Source-Alternativen zu oryxproject/oryx sind unter anderem: apache/flink — Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite… apache/spark — Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation… ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… alexrudall/ruby-openai — OpenAI API + Ruby! 🤖❤️ GPT-5 & Realtime WebRTC compatible! aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization… azure/mmlspark — Mmlspark is a distributed framework for executing machine learning models, data transformations, and AI service…
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
OpenAI API Ruby! 🤖❤️ GPT-5 & Realtime WebRTC compatible!
FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow
Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve