This project guides you from data inspection and preprocessing to crafting an end-to-end application for aircraft localization based on crowdsourced air traffic control communication data (ADS-B). It utilizes Apache Spark, Modin, ensemble methods, TabNet, Apache Kafka, Flask, and Leaflet.js.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
SciPhi-AI/R2R is a framework for rapid development and deployment of production-ready RAG pipelines. The framework enables the deployment, customization, extension, autoscaling, and optimization of RAG pipeline systems, making it easier for the OSS community to use them. It includes several code examples and client applications that demonstrate application deployment and interaction. The core abstractions come in the form of ingestion, embedding, RAG, and eval pipelines.
