[{"_1":2,"_68":-5,"_69":-5},"loaderData",{"_3":4,"_38":39,"_66":-5,"_67":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","light","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"needsLocaleCookieSync",false,"routes/_store+/_readers+/email_author.$slug/route",{"_40":41,"_65":-5},"book",{"_9":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzY4NzA","title","Deployment Intelligence","state","published","contactAuthorMethod","email","aboutText","

Your DORA metrics tell you your deployment frequency, your change failure rate, your mean time to restore. What they don't tell you is which deployment is about to fail.

Engineering leaders have spent a decade measuring what happened. Deployment Intelligence is about what's about to happen.

Drawing on academic research in Just-in-Time defect prediction, Google SRE practice, and data from millions of production deployments, this book introduces a new discipline for engineering leadership: the systematic, signal-based prediction of deployment risk before a single line of code reaches production.

You'll learn why the size of a change tells only a fraction of the story — and why signals like change entropy, database migration presence, author file-level expertise, and SLO error budget burn rate are the real predictors of production incidents. You'll understand why AI-assisted development is quietly increasing deployment risk at many organisations, and what to do about it.

Deployment Intelligence covers:

The 33 research-validated signals that predict deployment failure — ranked by predictive power
Why DORA metrics are necessary but insufficient for engineering leaders who want to prevent incidents, not just measure them
How to build a risk-aware deployment culture without slowing your teams down
The architecture of a deployment intelligence system — from signal collection to risk scoring to hard gates
The LLM semantic layer: why AI can read PR descriptions and identify \"quick fix,\" \"temporarily disabling,\" and \"bypassing validation\" as incident precursors
What the most resilient engineering organisations do differently on Fridays at 4pm

Whether you lead a team of 10 engineers or 10,000, the gap between \"we deploy a lot\" and \"we deploy safely\" is filled by intelligence — not instinct.

Andrew McCarron has spent over 20 years advising engineering organisations on delivery performance, risk management, and technical strategy. He is the founder of Koalr, an engineering intelligence platform built on the research and frameworks described in this book.

","authors",[53],{"_9":54,"_55":56,"_5":57},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6UHJvZHVjdEF1dGhvci84MjI3Mg","name","Andrew McCarron",{"_58":59},"avatar",{"_60":61,"_62":63,"_64":14},"url","https://d39qdlcrvnra4b.cloudfront.net/avatars/3697875/medium/avatar.jpg?1773947200","styleName","medium","exists","currentUser","routes/_store+/_layout","routes/_store+/_readers+/_layout","actionData","errors"]

Email the Author