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SKU/Artículo: AMZ-B0GPWPBYJB
Architecting Machine Learning Pipelines: Design Autonomous Data Systems for Enterprise AI and MLOps
Format:
Kindle
Detalles del producto
Disponibilidad:
Fuera de stock
Fuera de stock
Peso con empaque:
2.38 kg
2.38 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Architecting Machine Learning Pipelines: Design Autonomous Data Systems for Enterprise AI and MLOpsYour models may perform well in notebooks, but enterprise value is won or lost in production. When pipelines break under changing schemas, traffic spikes, dependency drift, or weak governance, even strong models become unreliable. If your team is still patching brittle workflows and firefighting failed jobs, you do not have an MLOps strategy yet—you have operational risk.Architecting Machine Learning Pipelines shows you how to design resilient, autonomous data systems that support modern enterprise AI. This book focuses on the architecture and operational patterns behind dependable machine learning delivery: real-time ingestion, contract-first data pipelines, orchestration, feature engineering, model lifecycle management, retrieval pipelines, observability, and governance. It treats ML pipelines as living systems built for continuous execution, not one-off scripts.You’ll learn how to build machine learning infrastructure that stays reliable under real-world pressure, while improving speed, control, and scalability across your AI stack. Inside, you will gain practical skills to:Design fault-tolerant, event-driven ML pipelines with idempotent processingBuild production-ready MLOps workflows with orchestration, registries, and feature storesIntegrate vector search, RAG pipelines, and agent-ready data systemsImplement continuous training, deployment strategies, and model serving patternsMonitor drift, lineage, and pipeline health with enterprise observability practicesSecure and govern ML systems with access controls, auditing, and compliance-aware controlsIf you are an engineer, architect, or technical leader building enterprise AI systems, this book gives you a clear path from fragile ML workflows to scalable, production-grade machine learning pipelines. Get your copy now and build an MLOps foundation your organization can trust.
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