This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
Batch LLM Inference with Ray Data LLM: From Simple to Advanced
Building Real-Time Inference Pipelines with Ray Serve
A Production-Ready, Scalable RAG-powered LLM-based Context-Aware QA App
Create Context-Aware Q&A Interfaces from Your Own Data with LLMs and Vector Embeddings - Includes an automated embedding pipeline and a model-powered Q&A interface
Production-grade scalable embedding API server using SentenceTransformers "intfloat/multilingual-e5-base" model, powered by Ray Serve for multi-GPU orchestration, with Prometheus & Grafana monitoring.
A drop-in replacement of fastapi to enable scalable and fault tolerant deployments with ray serve
A comprehensive guide to setting up and managing Raspberry Pi, Ray Clusters, and distributed AI workloads. Includes network troubleshooting, IP configuration, Ray Dashboard, and Python script execution for scalable AI applications.
Batch LLM Inference with Ray Data LLM: From Simple to Advanced
Ce projet propose une API intelligente construite avec FastAPI pour prédire des maladies à partir de données médicales de patients. L'application repose sur un modèle de machine learning (Logistic Regression) géré via MLflow, et peut facilement être déployée grâce à Docker.
This MLOps repository contains python modules intended for distributed model training, tuning, and serving using PyTorch and Ray, a distributed computing framework.
Ray Serve backend for Arabic Speech Recognition
Overview of our graduation project “Cairo Dictionary AI” – an Arabic dictionary enriched with AI. Includes our speech correction pipeline, HuggingFace models/datasets, backend prototypes (Ray & FastAPI), and academic report.
contains the basic structure that a model serving application should have. This implementation is based on the Ray Serve framework.
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