There are many ingredients for AI success, from selecting the best initial use case, to assembling a team with the right skills, to choosing the best infrastructure. Given the complexity, it’s easy to underestimate the critical role that data plays in the process.

This white paper describes the considerations for taking a deep learning project from initial conception to production, including understanding your business and data needs and designing a multistage data pipeline to ingest, prep, train, validate, and serve an AI model.