Distributed Embeddings at Scale - Processing 10M+ Rows Per Day with Ray, GPUs and Qdrant

This talk explains how to build and operate a production-grade, multi-platform NLP pipeline with Ray and GPU acceleration for large-scale embedding generation. It covers ingestion, preprocessing, distributed inference, vector-search storage, shard management, debugging, failure recovery, throughput tuning, and cost management.