Building and maintaining AI data pipelines is complex. This guide walks through what it takes, step by step, to turn raw multimodal data into AI-ready datasets.
What's inside
The facts and figures behind AI data today
Why traditional ETL pipelines don't fit AI data
Each stage of an AI data pipeline, from ingestion to delivery
Where human expertise belongs in an automated pipeline
Where should we send it?
Why it matters
What people working with AI data say
“Understanding your data's origin, its access history, and its management is fundamental. Developing AI data pipelines that not only meet ethical standards but also align with upcoming legal requirements is vital for sustainable progress.”
Lake DaiAdjunct Professor, Applied AI, Carnegie Mellon University
“Years of enterprise experience have taught me that models are only as good as their data. Data science teams spend on average over half their time cleaning and preparing data for processing.”
Avi ZurelDirector of Infrastructure, Hippo Insurance
“Having worked on several pioneering AI models, I am often reminded of the complexity involved in working with different types of data. The world ahead is multimodal.”
Andrew CoxR&D Systems Analyst, Sandia National Laboratories
“At first glance, pipelines seem simple. However, going even one layer in has shown us the dozens of different things that must go right in an AI data pipeline in order to deliver high-quality AI.”
