ML and data science teams often spend a disproportionate amount of their time curating, labeling, and preparing training data in order to build AI products and improve model performance. Learn what many leading AI teams have discovered which is that by employing a data engine to be the foundational infrastructure for how their team members interface with data and models — they can remove key bottlenecks and build these products remarkably fast.
Read this guide to discover a smarter, easier approach to creating and managing AI data, along with tried-and-true best practices, tools, and workflows that leading AI teams can use to visualize data, collaborate, automate parts of the labeling process, and build an optimized data engine.
How a data engine empowers you to understand and visualize your data, label data quickly and efficiently, as well as better train and evaluate models
Use cases and common features of a data engine
Who can benefit most from a data engine at your organization
What to look for in a data engine
The ROI of having a data engine and how it builds lasting competitive advantage