AI Scalability Handling Big Data for Intelligent Insights: Learn to scale AI systems for large-scale data processing - Couverture souple
Synopsis
Your models are only as powerful as your ability to scale them.
In AI Scalability: Handling Big Data for Intelligent Insights, you’ll learn how to design end-to-end AI systems that stay fast, reliable, and cost-efficient—even as data volumes soar and user demand spikes. From petabyte-scale pipelines to low-latency inference, this practical guide shows you how to turn big data into real-time intelligence.
Inside, you’ll discover how to:
Architect data pipelines for scale: batch + streaming (ETL/ELT), partitioning, sharding, caching, and lakehouse patterns.
Build distributed training with data/model/ pipeline parallelism (Spark, Ray, Dask) and efficient checkpointing.
Optimize feature engineering at scale with feature stores, vector search, and online/offline consistency.
Ship high-throughput inference using autoscaling microservices, asynchronous queues, and edge + cloud hybrids.
Cut latency with model optimization: quantization, pruning, mixed precision, distillation, and hardware acceleration (GPU/TPU).
Productionize with MLOps at scale: CI/CD for models, experiment tracking, lineage, reproducibility, and rollouts (canary/blue-green).
Observe and govern: monitoring, drift/outlier detection, data quality checks, cost controls, and compliance-ready governance.
Balance performance vs. spend with intelligent autoscaling, right-sizing, and workload-aware architectures.
Filled with field-tested patterns, sizing formulas, and checklists, this book equips data scientists, ML engineers, and platform teams to deliver AI that performs under real-world pressure—today and at tomorrow’s scale.
Who This Book Is ForML/AI engineers building large-scale training and inference systems
Data engineers designing high-volume pipelines and lakehouse platforms
MLOps/platform teams responsible for reliability, cost, and compliance
Technical leaders turning big data into fast, trustworthy decisions
Scale isn’t a luxury—it’s the difference between a demo and a durable product.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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AI Scalability Handling Big Data for Intelligent Insights (Paperback)
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Paperback. Etat : new. Paperback. Your models are only as powerful as your ability to scale them.In AI Scalability: Handling Big Data for Intelligent Insights, you'll learn how to design end-to-end AI systems that stay fast, reliable, and cost-efficient-even as data volumes soar and user demand spikes. From petabyte-scale pipelines to low-latency inference, this practical guide shows you how to turn big data into real-time intelligence.Inside, you'll discover how to: Architect data pipelines for scale: batch + streaming (ETL/ELT), partitioning, sharding, caching, and lakehouse patterns.Build distributed training with data/model/ pipeline parallelism (Spark, Ray, Dask) and efficient checkpointing.Optimize feature engineering at scale with feature stores, vector search, and online/offline consistency.Ship high-throughput inference using autoscaling microservices, asynchronous queues, and edge + cloud hybrids.Cut latency with model optimization: quantization, pruning, mixed precision, distillation, and hardware acceleration (GPU/TPU).Productionize with MLOps at scale: CI/CD for models, experiment tracking, lineage, reproducibility, and rollouts (canary/blue-green).Observe and govern: monitoring, drift/outlier detection, data quality checks, cost controls, and compliance-ready governance.Balance performance vs. spend with intelligent autoscaling, right-sizing, and workload-aware architectures.Filled with field-tested patterns, sizing formulas, and checklists, this book equips data scientists, ML engineers, and platform teams to deliver AI that performs under real-world pressure-today and at tomorrow's scale.Who This Book Is ForML/AI engineers building large-scale training and inference systemsData engineers designing high-volume pipelines and lakehouse platformsMLOps/platform teams responsible for reliability, cost, and complianceTechnical leaders turning big data into fast, trustworthy decisionsScale isn't a luxury-it's the difference between a demo and a durable product. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798262024988
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