This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
AI-based search engine done right
Building Real-Time Inference Pipelines with Ray Serve
A Production-Ready, Scalable RAG-powered LLM-based Context-Aware QA App
Custom AI Generator -- Pretrain your LLM Models with this Automated Embedding Generator and model Q&A Interface. Uses Retrieval Augmented Generation (RAG) to reduce hallucinations and ground the LLM on a source of truth
A drop-in replacement of fastapi to enable scalable and fault tolerant deployments with ray serve
contains the basic structure that a model serving application should have. This implementation is based on the Ray Serve framework.
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.
This MLOps repository contains python modules intended for distributed model training, tuning, and serving using PyTorch and Ray, a distributed computing framework.
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