debnsuma/ray-for-developers
A comprehensive hands-on guide to building production-grade distributed applications with Ray - from distributed training and multimodal data processing to inference and reinforcement learning.
This is a practical guide for machine learning engineers and data scientists to build and deploy large-scale AI applications. It helps you take raw data and machine learning models, distribute computations across many machines, and produce highly scalable training pipelines, data processing workflows, and model deployments. It's for anyone who needs to handle huge datasets or deploy complex AI models in production.
Use this if you are a software or machine learning engineer struggling to scale your Python or AI applications and need a comprehensive, hands-on guide to distributed computing.
Not ideal if you are looking for an introductory guide to machine learning concepts or prefer theoretical explanations over practical, code-based examples.
Stars
19
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/debnsuma/ray-for-developers"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
qualcomm/ai-hub-models
Qualcomm® AI Hub Models is our collection of state-of-the-art machine learning models optimized...
petuum/adaptdl
Resource-adaptive cluster scheduler for deep learning training.
zszazi/Deep-learning-in-cloud
List of Deep Learning Cloud Providers
lincc-frameworks/hyrax
Hyrax - A low-code framework for rapid experimentation with ML & unsupervised discovery in astronomy
openhackathons-org/gpubootcamp
This repository consists for gpu bootcamp material for HPC and AI