{"product_id":"model-registry-engineering-yousf-alrukh-9798175646697","title":"Model Registry Engineering: Artifact Tracking, Metadata, Lineage, Promotion Workflows, and Governance for ML Models","description":"Model Registry Engineering is a systems-oriented guide to the infrastructure that determines whether your team can answer, months after a deployment, exactly what model was running and why. \u003cp\u003e\u003c\/p\u003eA model registry is not a file server with version numbers. It is the system of record connecting every production model back to the training run, data version, code commit, and approval that produced it - and forward to every environment it was deployed to. \u003cp\u003e\u003c\/p\u003eThirteen chapters cover the full registry stack: \u003cp\u003e\u003c\/p\u003e- Registry foundations: what constitutes a complete artifact, immutable versioning, and lifecycle stages that mean something\u003cbr\u003e- Artifact tracking: weights, configs, tokenizers, and checksum verification at load time\u003cbr\u003e- Metadata: metrics with their evaluation protocol, dataset versions, framework and hardware context\u003cbr\u003e- Lineage: training runs, data versions, code commits, and dependency locks - queryable in both directions\u003cbr\u003e- Promotion workflows: candidate gates, staging parity, registry-driven deployment, and evidence-backed approval\u003cbr\u003e- Governance: access control, risk-scaled review, retention policy, and complete audit trails\u003cbr\u003e- Deployment integration: release pipelines, serving reconciliation, and rollback as a first-class operation\u003cbr\u003e- Registry quality: completeness, consistency, staleness detection, and write-time validation\u003cbr\u003e- Failure modes: orphan artifacts, wrong lineage, untracked copies, and stage drift\u003cbr\u003e- Registry APIs and automation: required fields in signatures, idempotent writes, and event streams\u003cbr\u003e- Multi-team registries: namespace design, shared base models, cost attribution, and visibility defaults\u003cbr\u003e- Cost and scale: storage growth, deduplication, retrieval patterns, and archival economics\u003cbr\u003e- Production operations: backup and restore, safe migration, correctness monitoring, and policy change management \u003cp\u003e\u003c\/p\u003eEvery section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and a short set of practical checks. Each chapter closes with a worked scenario showing how these decisions interact under real pressure. \u003cp\u003e\u003c\/p\u003eThree appendices cover architectural comparisons (centralized versus federated, content-addressed versus sequential versioning, registry-pull versus image-baked deployment), a four-level registry maturity model, and adoption sequencing for teams starting from scratch. \u003cp\u003e\u003c\/p\u003eWritten for ML platform engineers, MLOps practitioners, and technical leads responsible for what actually runs in production.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Yousf Alrukh\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798175646697\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/19\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 82\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.27lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.17d","brand":"Yousf Alrukh","offers":[{"title":"Paperback","offer_id":50780077293823,"sku":"9798175646697","price":12.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/model-registry-engineering-yousf-alrukh-9798175646697","provider":"WR Book House","version":"1.0","type":"link"}