This e-book goes layer by layer through the specific engineering decisions across ingestion, storage, transformation, orchestration, serving and governance that determine whether AI workloads reach production and stay there.
This guide is written for engineering leaders who are already investing in data infrastructure -- and who are trying to understand why their AI initiatives are not reaching production at the rate they should be. It is not an introduction to data engineering concepts or a vendor evaluation guide.
Where we go deep into specific tools, patterns, and failure modes, there is a reason for it. Where we stay at the architectural and decision level, that is intentional too. The guide is structured around the decisions that matter -- not a comprehensive implementation manual for any single layer of the stack.
