AI/ML Engineer: An Intermediate's Guide to Closing the Deployment Gap — Ebook (EPUB + PDF)
You can already train a model that performs well in a notebook. That's the part every course teaches. What almost none of them cover is the part that actually fills an ML engineer job title: keeping that model working once it has users, when nobody's watching the dashboard. Only about 53% of AI projects that reach a working prototype ever make it into production at all — and the ones that do ship still fail silently more often than they fail loudly, through training-serving skew, a stale feature nobody re-validated, or a proxy metric that quietly stopped measuring what it was built to measure.
This book closes that gap by building one habit across nine chapters and never letting go of it: name the category before you touch the fix. You'll learn the production loop of experimentation, deployment, monitoring, and retraining, and the three ways a live model actually breaks — input drift, prediction drift, performance degradation — through a lending company's three-source income field, a bank's underwriting-copilot near-miss, an ad-tech pipeline's overnight 8% drop, and a claims-triage model quietly scoring on a redefined field. It closes with a full production-debugging framework and an ML system-design framework, worked start to finish, so the diagnostic instinct you build here is already automatic before an interviewer asks you to prove it.
Inside
- The production loop and the three drift types — input drift, prediction drift, performance degradation — walked backward through a real 8%-overnight-drop incident (Chapter 1)
- The pipeline jungle made concrete: a lending company's three-source income field, a backfill idempotency bug, and the schema-check, backfill, and SQL discipline that keeps a growing pipeline honest (Chapter 2)
- The real MLOps stack without the wallpaper — PyTorch vs. TensorFlow as an actual tradeoff, and where Docker, Kubernetes, MLflow, Airflow/Argo, Terraform, Kubeflow, and managed platforms like SageMaker each do or don't earn their place (Chapter 3)
- LLMOps as an engineering discipline — governance enforced as infrastructure, continuous online evaluation, and single-responsibility multi-agent design — built around a bank underwriting-copilot's real compliance near-miss (Chapter 4)
- The LLM specialization track: RAG vs. LoRA/QLoRA as two different problems, the pgvector/Qdrant/Weaviate landscape, and an honest line around what's deliberately out of scope at this tier (Chapter 5)
- Two corrective job-search findings — why Kaggle rank underweights for ML-engineer hiring, and what an eleven-tool requirements list actually tells you — plus a worked Production Debugging Framework, ML System Design Framework, and RAG failure triage for the interview room (Chapters 6-9)
Written for engineers who can already train a model and want to own what happens after it ships — deployment, monitoring, retraining, and the interview room where all of it gets tested.
What you get: the complete ebook as EPUB (Kindle, Kobo, Apple Books and every reader app) and as PDF, delivered the moment you pay. Book 2 of the From Experience to Evidence AI/ML Engineer series.
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