Component 4: Observability

Create trust through observability

Core ideas

Effective AI systems require both software-style observability and data-science evaluation habits; each discipline has gaps when generative AI moves fast.
Engineers often excel at what happened but struggle with why probabilistic systems behave as they do; data scientists face the inverse when production demands cross-service telemetry and cost granularity.
Chapter 9 adapts engineering observability, including phases of the AWS Observability Maturity model, for AI’s non-deterministic realities.
Chapter 10 carries ML evaluation forward: benchmarks, metrics, experiments, feedback, and how to weigh structured evidence against “vibes” and anecdotes.
Motto for the AI engineer: Think like a data scientist, build like a software engineer.

Builders should exchange crafts: synthesize the strongest methods from engineering and data science, reframed for generative AI.

Full narratives on both disciplines’ pain points, the maturity framework walkthrough, and evaluation practice in Chapters 9 and 10.