This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

Multi-backend LLM serving and training platform — vLLM/Triton/Ray Serve/KServe/BentoML behind one contract, Kueue/Karpenter GPU orchestration, Ray Train/FSDP/DeepSpeed with LoRA/PEFT and DVC, MLflow/W&B tracking, and a tool-grounded LangGraph advisor — CI-validated without real GPU cost.

This repository showcases hands-on projects leveraging distributed multi-GPU training to fine-tune large language models (LLMs).

Analyzing sentiment from Twitter data presents key challenges, including handling large-scale, noisy text and the absence of labeled data. This project addresses these issues through a two-phase pipeline.

This MLOps repository contains python modules intended for distributed model training, tuning, and serving using PyTorch and Ray, a distributed computing framework.

Encoder-Decoder model for pinterest image recommender system using active learning. Built with PyTorch and Ray