Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models - Softcover
Inhaltsangabe
Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models
Why do so many AI initiatives fail, not because the models are wrong, but because the data behind them can’t be trusted?
Every data professional has faced it: a model that performs perfectly in testing but unravels in production. The culprit isn’t magic; it’s weak data foundations. Without structured, governed, and observable data pipelines, even the smartest algorithms crumble under drift, latency, and inconsistency.
Data Foundations for AI Systems is the definitive practical guide to building machine learning pipelines that work reliably, every time. It translates the complex, often chaotic reality of AI data operations into clear, actionable engineering principles grounded in production experience.
Through real-world patterns, reproducible frameworks, and field-tested strategies, this book shows how to architect systems where data quality, versioning, observability, and scalability are built in, not bolted on. It bridges the gap between data engineering, data science, and MLOps, helping you create infrastructure that empowers, not obstructs, your models.
You’ll learn how to:
- Design scalable data pipelines that serve both training and inference workloads.
- Build feature stores that ensure consistent, reusable model inputs.
- Enforce data contracts, lineage, and quality gates across every stage of the pipeline.
- Implement versioning, reproducibility, and rollback strategies that make audits effortless.
- Monitor data and model drift in production before performance collapses.
- Align data engineering and machine learning teams through shared metrics and SLAs.
If you’re a data engineer, machine learning practitioner, or technical leader tired of firefighting broken pipelines and inconsistent results, this book delivers the frameworks and practices you need to build dependable, production-grade AI systems.
Build your competitive edge on reliable data, not reactive fixes.
Your AI models are only as strong as the pipelines beneath them, make them unbreakable.
👉 Buy Data Foundations for AI Systems today and start engineering data pipelines that scale, adapt, and deliver trustworthy machine learning outcomes.
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Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models
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Data Foundations for AI Systems (Paperback)
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Paperback. Zustand: new. Paperback. Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models Why do so many AI initiatives fail, not because the models are wrong, but because the data behind them can't be trusted?Every data professional has faced it: a model that performs perfectly in testing but unravels in production. The culprit isn't magic; it's weak data foundations. Without structured, governed, and observable data pipelines, even the smartest algorithms crumble under drift, latency, and inconsistency.Data Foundations for AI Systems is the definitive practical guide to building machine learning pipelines that work reliably, every time. It translates the complex, often chaotic reality of AI data operations into clear, actionable engineering principles grounded in production experience.Through real-world patterns, reproducible frameworks, and field-tested strategies, this book shows how to architect systems where data quality, versioning, observability, and scalability are built in, not bolted on. It bridges the gap between data engineering, data science, and MLOps, helping you create infrastructure that empowers, not obstructs, your models.You'll learn how to: Design scalable data pipelines that serve both training and inference workloads.Build feature stores that ensure consistent, reusable model inputs.Enforce data contracts, lineage, and quality gates across every stage of the pipeline.Implement versioning, reproducibility, and rollback strategies that make audits effortless.Monitor data and model drift in production before performance collapses.Align data engineering and machine learning teams through shared metrics and SLAs.Each chapter walks you through a vital layer of a modern AI data stack, from ingestion to serving, complete with real-world case studies and design templates you can adapt immediately.If you're a data engineer, machine learning practitioner, or technical leader tired of firefighting broken pipelines and inconsistent results, this book delivers the frameworks and practices you need to build dependable, production-grade AI systems.Build your competitive edge on reliable data, not reactive fixes.Your AI models are only as strong as the pipelines beneath them, make them unbreakable. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798271989551
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Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models
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Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models
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Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 51830885
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Data Foundations for AI Systems (Paperback)
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Paperback. Zustand: new. Paperback. Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models Why do so many AI initiatives fail, not because the models are wrong, but because the data behind them can't be trusted?Every data professional has faced it: a model that performs perfectly in testing but unravels in production. The culprit isn't magic; it's weak data foundations. Without structured, governed, and observable data pipelines, even the smartest algorithms crumble under drift, latency, and inconsistency.Data Foundations for AI Systems is the definitive practical guide to building machine learning pipelines that work reliably, every time. It translates the complex, often chaotic reality of AI data operations into clear, actionable engineering principles grounded in production experience.Through real-world patterns, reproducible frameworks, and field-tested strategies, this book shows how to architect systems where data quality, versioning, observability, and scalability are built in, not bolted on. It bridges the gap between data engineering, data science, and MLOps, helping you create infrastructure that empowers, not obstructs, your models.You'll learn how to: Design scalable data pipelines that serve both training and inference workloads.Build feature stores that ensure consistent, reusable model inputs.Enforce data contracts, lineage, and quality gates across every stage of the pipeline.Implement versioning, reproducibility, and rollback strategies that make audits effortless.Monitor data and model drift in production before performance collapses.Align data engineering and machine learning teams through shared metrics and SLAs.Each chapter walks you through a vital layer of a modern AI data stack, from ingestion to serving, complete with real-world case studies and design templates you can adapt immediately.If you're a data engineer, machine learning practitioner, or technical leader tired of firefighting broken pipelines and inconsistent results, this book delivers the frameworks and practices you need to build dependable, production-grade AI systems.Build your competitive edge on reliable data, not reactive fixes.Your AI models are only as strong as the pipelines beneath them, make them unbreakable. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798271989551
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