High-performing machine learning models rely on AI data pipelines. This whitepaper, produced in collaboration with Google, explains how pipelines produce robust ML models.
What's inside
Why model quality starts with data quality
Tracking data origin, access history, and management
Building pipelines ready for ethics rules and third-party audits
Working across images, text, audio, and many formats
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.”
