Overview

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 For ML/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.

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