AI Training Data Pipeline

System Design Deep Dive — #1 of 20 | This is part of a 20-post series covering the most critical system design topics. Follow to get the next one. Google's research on data quality -- later formalized in their "Data Cascades" paper (NeurIPS 2021) -- showed that data quality issues compound through ML pipelines, causing cascading failures that are expensive to debug. Andrew Ng has been championing the "data-centric AI" shift since 2021, arguing that for most practical applications, improving data quality yields better results than improving model architecture. Yet most ML teams still spend 80% of effort on model tuning and 20% on data. The teams actually shipping reliable AI products flip that ratio. TL;DR: An AI training data pipeline is a purpose-built system for ingesting, validating, transforming, labeling, versioning, and serving training data. It's not ETL with a machine learning label -- it's the most impactful investment you can make in your ML infrastructure. Get the data pipel