The Open Source Feature Store for AI/ML
- machine-learning
- features
- ml
- big-data
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The Open Source Feature Store for AI/ML
Asynchronous Python framework for event-driven services. A thin client for Kafka, RabbitMQ, NATS, Redis and MQTT with full access to native broker features, plus AsyncAPI docs, in-memory tests and observability out of the box.
Monad, Functional Programming features for Golang
Real-time decision features without streaming infra. Turn live events into product reflexes — no Kafka, no Flink, no feature store.
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.