Streaming Data for Real-Time AI Applications by Blaize Stewart(.ePUB)+

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File Size: 10 MB

Streaming Data for Real-Time AI Applications: Performant, Efficient, and Reliable Apps for Real-Time Intelligence by Blaize Stewart, Bipin Singh
Requirements: .ePUB, .PDF, .MOBI/.AZW reader, 10 MB
Overview: Real-time streaming data is transforming how organizations leverage AI by enabling continuous ingestion, processing, and analysis of information as it’s generated, maximizing the speed and precision of business decisions. But building and deploying these systems comes with unique challenges, including managing low-latency data pipelines, optimizing AI models for live environments, and ensuring scalability under high-throughput demands. This report helps you harness the power of streaming data and AI technologies to deliver measurable business impact. Data storage architecture directly influences the performance and scalability of AI systems. Real-time AI uses in-memory processing with minimal data persistence, handling events quickly and discarding them to maintain low latency. Batch AI relies on persistent storage, processing large, historical datasets for deeper analysis and model training. Streaming systems must balance speed with minimal retention, sometimes using summaries or short-lived states.
Genre: Non-Fiction > Tech & Devices

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