Claims Browser

There are practical recipes for AI-assisted network automation and development.

"AI Networking Cookbook — Practical recipes for AI-assisted network automation and development"

The LLM era requires production-grade AI architecture and system design patterns.

"System Design for the LLM Era — Patterns and Principles for Production-grade AI Architecture"

Packt Publishing has best-selling AI/ML books.

"Find more best-selling AI/ML books by @PacktPublishing @PacktDataML here:"

The book 'Distributed AI Systems' is a practical guide for building scalable training, inference, and serving systems for production AI.

"Distributed AI Systems — A practical guide to building scalable training, inference, and serving systems for production AI"

Google Cloud Platform offers significant scalability, but managing costs effectively is critical to realizing its value.

"Google Cloud Platform offers significant scalability, but managing costs effectively is critical to realizing its value."

LLMOps can be learned by diving into core MLOps principles

"Get started with LLMOps by diving into core MLOps principles"

A cost-efficient, hardware-accessible, and open-source pretraining recipe has been missing from the field

"a cost-efficient, hardware-accessible, and open-source pretraining recipe has long been missing"

Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communities

"Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communities"

WSI embedding extraction is fundamentally a data-centric systems problem rather than a compute-bound workload, with storage dominating beyond moderate concurrency

"By characterizing the scaling envelope, we demonstrate that storage dominates beyond moderate concurrency, reframing WSI embedding extraction as a data-centric systems problem rather than a purely compute-bound workload."

Distributed vector databases with metadata-coupled embeddings are particularly beneficial for low-resource environments in computational pathology

"This representation database is compact and reusable for tasks such as retrieval, classification, and few-shot learning, particularly benefiting low-resource environments."

Progress in vision generative AI has been driven by output quality, with hardware evolving reactively to accommodate growing model demands

"progress in vision generative AI has been driven by output quality, with hardware evolving reactively to accommodate growing model demands"

Vision generative models are equally needed in applications that operate under strict hardware constraints at the edge, including autonomous vehicles, agricultural sensors, and mobile devices

"vision generative models are equally needed in applications that operate under strict hardware constraints at the edge, including autonomous vehicles, agricultural sensors, and mobile devices"

The high latency of LLM calls inverts a classical constraint in adaptive query processing where online learners had to stay lightweight

"The latency window inverts a design constraint of classical adaptive query processing, where online learners had to stay lightweight to avoid dominating the predicates they optimize."

Event-based object classification using Spiking Neural Networks on neuromorphic devices can solve issues of size, weight, power consumption, security, latency, and connectivity present in conventional frame-based computer vision approaches

"The use of Spiking Neural Networks (SNNs) hosted on neuromorphic devices attempts to solve several issues present in this conventional approach."

Enterprise AI deployment is fundamentally a coordination problem across multiple organizational functions including business units, application teams, AI teams, testing, platform engineering, infrastructure, security, operations, and data governance.

"Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, security, operations, and data governance."

A runtime architecture based on four responsibility objects (Skill, Harness, Scaffold, and external data substrate) can serve as shared organizational contracts for enterprise AI deployment.

"We present four responsibility objects as shared organizational contracts: Skill (reusable, versioned capability and workflow asset), Harness (runtime compiler and governor), Scaffold (execution/control boundary and NFR owner), and a stack-external data substrate under independent CIO-governed semantics and telemetry."

Within a declared operating region, it is possible to achieve cost-aware capability-capacity separability where changing activated capability preserves capacity-response interaction within a preregistered equivalence margin, and changing compatible Scaffold capacity preserves capability semantics up to a non-inferiority margin.

"within a declared operating region, changing activated capability preserves the capacity-response interaction within a preregistered equivalence margin, while changing compatible Scaffold capacity preserves capability semantics up to a non-inferiority margin, and the required controls stay within a declared enforcement budget."

A cluster-period randomized crossover experiment with balanced order, reset/washout, repeated seeds and failure regimes can produce a four-state verdict (supported, falsified, conditional-engineering, or inconclusive) for testing the cost-aware capability-capacity separability hypothesis.

"We propose a cluster-period randomized crossover experiment (balanced order, reset/washout, repeated seeds and failure regimes, cluster-aware uncertainty) with a four-state verdict: supported, falsified, conditional-engineering, or inconclusive."