Scaling Pinterest ML Infrastructure with Ray: From Training to End-to-End ML Pipelines

This blog post discusses how Pinterest expanded its machine learning (ML) infrastructure with Ray, addressing challenges such as slow data pipelines and inefficient compute usage. It highlights technical innovations that improve feature development, sampling, and label modeling, ultimately enhancing the efficiency and scalability of ML workflows at Pinterest, achieving up to a 10x reduction in iteration time.