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Martin D: Bears & Mosquitoes

Bears & Mosquitoes

Building Trusted AI from the Inside Out - A Field Guide to AI That Earns Its Keep

Running a large language model on hardware you control is no longer exotic, but the gap between a model that loads and a service that holds up under load is where most self-hosting efforts stall. This book closes that gap by building the thing, one working system that grows across all fifteen chapters.You begin with an 8B model on a single card, the kind of hardware you can rent for pennies an hour or already own. From there the book adds what real traffic demands: an inference engine chosen on its merits, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves past what one card can hold, placing a 70B deployment across a pool of GPUs selected on the network between them rather than the sum of their memory, and shopping for that capacity across several providers at once.The final part goes to the frontier. You will serve a 753-billion-parameter sparse model on eight rented H200s, then measure what it costs per session as concurrency climbs, and find the point where a larger batch stops making each session cheaper. That result explains how commercial inference providers price what they sell, and it tells you when self-hosting wins and when it does not.Costs are measured rather than estimated throughout. Every figure in the final part is regenerated from a committed data file, and the book says plainly which numbers are measurements and which are predictions. The complete control plane is available as an open-source companion repository, written in Go, so you can run what you read.You Will:<ul><li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li><li>Learn how inference actually consumes a GPU, covering KV cache growth, quantization formats and their real cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li><li>Measure the economics from real runs, working out per-session cost as batch size and context length grow, and identifying the point where self-hosting stops beating a commercial API.</li></ul>This book is for:Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Running a large language model on hardware you control is no longer exotic, but the gap between a model that loads and a service that holds up under load is where most self-hosting efforts stall. This book closes that gap by building the thing, one working system that grows across all fifteen chapters.You begin with an 8B model on a single card, the kind of hardware you can rent for pennies an hour or already own. From there the book adds what real traffic demands: an inference engine chosen on its merits, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves past what one card can hold, placing a 70B deployment across a pool of GPUs selected on the network between them rather than the sum of their memory, and shopping for that capacity across several providers at once.The final part goes to the frontier. You will serve a 753-billion-parameter sparse model on eight rented H200s, then measure what it costs per session as concurrency climbs, and find the point where a larger batch stops making each session cheaper. That result explains how commercial inference providers price what they sell, and it tells you when self-hosting wins and when it does not.Costs are measured rather than estimated throughout. Every figure in the final part is regenerated from a committed data file, and the book says plainly which numbers are measurements and which are predictions. The complete control plane is available as an open-source companion repository, written in Go, so you can run what you read.You Will:<ul><li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li><li>Learn how inference actually consumes a GPU, covering KV cache growth, quantization formats and their real cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li><li>Measure the economics from real runs, working out per-session cost as batch size and context length grow, and identifying the point where self-hosting stops beating a commercial API.</li></ul>This book is for:Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Building and Operating Production AI Systems from a Single GPU to Multi-Node Frontier Models

Self-Hosted LLM Inference

Wiley Finance

The handbook to unleash the power of AI in Finance with guidance to pragmatically managing AI Risk Are you ready to harness the transformative potential of artificial intelligence and machine learning in finance while effectively managing the associated risks? "AI and Risk Management in Finance: A Practitioner's Handbook" by Sri Krishnamurthy, CFA, is your indispensable guide. Tailored for financial professionals, this comprehensive handbook delivers practical insights, case studies, and frameworks to navigate the complex landscape of AI and ML in the financial sector. This pragmatic handbook, developed primarily for professionals involved in designing, developing, and deploying AI models in the financial industry explores cutting-edge technologies, regulatory landscapes, and pragmatic frameworks. From generative AI to robust risk management strategies, this handbook equips you with the tools you need to validate models, ensure compliance, and operationalize AI risk management effectively. Leveraging lessons from QuantUniversity's AI and Machine Learning Risk Management program offered in 18 countries, this book focuses on enabling practitioners apply the state-of-the-art AI and machine learning techniques factoring unique risks specific to the financial services domain. Whether you're a seasoned professional or a newcomer, this book offers invaluable insights for harnessing the power of AI responsibly in the financial industry. Highlights of the Book: <ul> <li>Machine Learning and AI in Financial Services: Dive into the fundamentals of ML and AI, exploring various applications within the financial industry through real-world case studies.</li> <li>Generative AI and LLMs in Finance: Understand the emerging landscape, key players, and advanced concepts like prompt engineering and fine-tuning with practical examples.</li> <li>Risk Management for AI/ML Applications: Learn about current and emerging regulatory landscapes, AI risk management frameworks, and legislative efforts shaping the field.</li> <li>Data Governance Issues: Address critical aspects of data quality, privacy, security, and governance, with actionable frameworks and illustrative case studies.</li> <li>Model and System Governance: Gain insights into validating machine learning models, quantifying model risk, and ensuring robust system governance with MLOps and model monitoring.</li> <li>Key Risk Assessment Facets: Focus on documentation, explainability, fairness, security, and reliability with detailed case studies.</li> <li>Verification and Validation: Distinguish between verification and validation and learn frameworks for effective AI application validation.</li> <li>Algorithmic Auditing: Conduct comprehensive algorithmic audits with detailed processes and real-world examples.</li> <li>Operationalizing AI Risk Management: Discover ten essential strategies for implementing AI risk management practices effectively in your organization.</li> </ul> Prepare for the future of finance with expert advice and actionable strategies. Don't miss out on this comprehensive guide-your roadmap to navigating AI and risk management in the financial sector.

A Practitioner's Guide to Machine Learning, Generative AI, Agentic Systems, and Responsible Deployment

AI and Risk Management in Finance

AI and quantum computing are two disruptive forces about to push financial services and the banking industry over the edge, demanding an unprecedented transformation. They are not fleeting trends but fundamental disruptors that will reshape the core of banking and finance. Authored by a senior Swiss banking technology executive, this practical guide helps board members and senior leaders turn disruption into a disciplined strategic agenda. It offers a rare insider perspective and a roadmap for navigating these seismic shifts. Bridging the gap between technological innovation and practical application, this book equips leaders with the tools to address the challenges and opportunities posed by AI and quantum computing. It features a thorough review of the latest business opportunities and threats associated with these technologies, alongside foresight-based strategic decision frameworks to guide prioritisation. Real-world case studies and an online companion with 125 AI use cases tailored for banks provide actionable insights. It also introduces an original AI Adoption Maturity Model and a Hybrid Enterprise Reference Architecture to ensure safe integration of internal systems with external frontier models. Finally, it provides two detailed multi-year roadmaps, offering a clear path to harness AI's potential and to prepare for a post-quantum future through crypto agility.Rather than offering abstract predictions or generic solutions, this book presents financial-services leaders with the frameworks, choices, and implementation path they need to act with confidence at a moment when waiting is no longer an option.

Evolutions at the Bleeding Edge

How AI and Quantum Computing Will Make or Break the Bank

Decision Before Consequence

How the rise of AI agents--and other forms of autonomous intelligence--will transform every aspect of how companies create value and compete.Companies have long operated with a basic assumption: intelligence is scarce. Talent is limited, expertise is expensive, and good judgment is hard to scale. But that world is ending.AI agents are bringing a new form of labor into the enterprise--one that can work autonomously, learn continuously, and scale instantly. Their embodied counterparts, from drones to humanoid robots, are doing the same in the physical world. Together, these systems are introducing a new economic reality: cognition is becoming cheap, elastic, and abundant.In Abundant Intelligence, Deloitte's Nitin Mittal and futurist Mike Walsh show what happens when intelligence becomes cheap and widely available. Drawing on inside access to the people leading AI transformations at the world's most powerful companies, they uncover how this shift is challenging every assumption about strategy, productivity, and leadership, and what organizations must do now to stay ahead.As abundance becomes the new normal, the leader's job shifts from collecting intelligence to directing it. Those who learn to harness this expanding workforce of digital labor will unlock new levels of speed, creativity, and advantage. For everyone else, the gap will widen fast.

How Digital Labor Will Rewrite the Rules of Business

Abundant Intelligence

Nearly every financial institution is racing to put AI into production. Most programs stall, and the gap is rarely technical.

A CEO asks her executive team whether they are falling behind on AI, and the room erupts. The technology chief lists vendors. The risk officer wants to know who approved the model running in consumer lending. The head of people reports that three high performers just resigned, citing fear of being replaced. Every person at that table is experiencing AI as a threat to something they care about, and nobody has given them a shared way to think about what comes next.

Human in the Lead argues that the hardest problems in financial services AI are not algorithmic. They are human: employees who do not trust the systems, customers who feel processed instead of served, and oversight that performs compliance without delivering it. In an industry where a flawed model does not recommend a bad film but denies a mortgage, freezes an account, or rejects an insurance claim, moving fast and breaking things is not a strategy.

Drawing on fifteen years of technology transformation inside banks, wealth managers, and insurers, Smit Jani sets out a practical operating model for AI adoption that regulators respect, employees embrace, and customers trust.

Inside this book:
Three models of human involvement, human-in-the-loop, human-in-command, and human-in-the-lead, with a decision framework for matching the right model to each use case.
Governance foundations that make AI systems audit-ready and aligned with regulatory expectations without strangling innovation.
A ninety-day roadmap that takes a pilot from leadership alignment through controlled launch to a scaling decision.
Role redesign playbooks for relationship managers, contact center teams, operations staff, and risk and compliance professionals.
A balanced scorecard that measures trust, employee adoption, and customer outcomes alongside efficiency, because what you measure shapes what your AI becomes.
Case vignettes from banking, wealth management, and insurance, including the operations program that nearly failed and the fraud system that balanced detection with fairness.

A complete implementation toolkit of templates, checklists, and maturity models, plus a governance readiness framework for the coming wave of agentic AI.

Written for chief executives, board members, risk and compliance leaders, and the transformation teams who answer to them. Each chapter stands alone, making the book equally useful read cover to cover or kept on the desk as a working reference.

Smit Jani has spent fifteen years leading technology transformation at financial institutions across North America, from global banks to wealth managers. He is a management consultant at eClerx, leading an AI transformation and automation squad at a global investment bank. The frameworks in this book were built and tested inside live regulated environments, not theorized from outside them.

The institutions that win with AI will not be the ones that automate the most. They will be the ones that keep humans in the lead.

The Executive Playbook for Safe, People-First AI in Financial Services

Human In The Lead

Artificial intelligence has crossed the liability Rubicon. It is no longer a backend IT experiment governed by soft ethics, voluntary frameworks, and vague policy statements; it is a material financial asset driving enterprise revenue and unprecedented legal exposure. As we transition into Industry 5.0, algorithms are making autonomous, consequential decisions at machine speed. Yet, corporate boards and risk committees are attempting to govern these gigahertz systems using static compliance checklists designed for a slower, simpler era. The result is a looming crisis of algorithmic discrimination, regulatory friction, and catastrophic reputational decay. When an opaque, black-box model hallucinates financial advice or leaks proprietary trade secrets, the ensuing failure cascade can destroy billions in market capitalization overnight.AI Governance and Risk Management provides the definitive, boardroom-ready blueprint for surviving the fragmented global regulatory dragnet. This book dismantles the illusion of traditional "Human-in-the-Loop" oversight, proving that true algorithmic safety requires hard, architectural constraints. Drawing on decades of technology operations and academic leadership, the text translates classical financial auditing principles into executable machine logic. The author brings unparalleled practical experience-from supporting six Silicon Valley startups in client-facing roles to develop computer vision use cases, to leading over 30 continuous improvement projects for Cisco Systems. Having authored foundational texts on Segregation of Duties (SOD) and Hierarchical SOD two decades ago, and backed by 15 years of teaching financial risk management, this expertise culminates in the AI Integrity Framework (AIIF). This Hamiltonian approach empowers organizations to embed continuous improvement cycles and strict SOD directly into their deployment pipelines.Readers will discover how to engineer automated "Build Breaks," architect deterministic kill switches, and secure the unstructured data supply chain against copyright contamination and intellectual property theft. By shifting from reactive public relations to proactive "Governance as Code," Chief Risk Officers, legal counsel, and audit committees can mathematically prove fiduciary control to regulators across overlapping jurisdictions, from California to the European Union.Authored by a veteran industry practitioner, creator of five previous Wiley &amp; Sons texts, and current developer of the Industry 5.0 and AI curriculum at Santa Clara University, this release is not a theoretical ethics manual. It is an uncompromising operational survival guide. It provides the exact structural tools necessary to transform artificial intelligence from an unquantified off-balance-sheet liability into a fully auditable, legally defensible, and highly profitable enterprise asset.

The AI Integrity Framework (AIIF) for Enterprise Control and Regulatory Compliance

Auditable AI Governance and Risk Management

Most AI governance is built around the wrong object.

Enterprises watch the model. They should be governing the crossing-the moment-generated language asks to become institutional reality.

The Bounding Gap exposes the substitutions that pass for AI safety: observation for control, behavior modification for enforcement, explanation for proof, and human review after the decisive movement has already begun. It relocates governance from the model itself to the boundary where generated possibility becomes institutional consequence.

Through a series of scientific inversions, the book develops a new architecture of authority. Gödel shows why a system cannot certify itself. Turing is inverted to ask not whether machines can imitate human judgment but whether their outputs possess standing to enter institutional life. Wilson's correlation geometry explains why modern AI cannot be governed through the old imagination of local logic and linear control. Hooke and Payne-Gaposchkin reveal why observation without the correct interpretive structure remains blind.

These arguments converge on one claim:

Authority cannot live inside the generative field. It must be external, deterministic, and enforceable before consequence becomes real.

The book also names the danger created by speed. Synthesis entropy and governance dilation describe what happens when machine-generated states multiply faster than institutions can establish admissibility. A rationale can now be produced in milliseconds, while responsibility, evidence, and authority remain unresolved.

For enterprise architects, Chief Compliance Officers, General Counsels, Chief Risk Officers, AI founders, and regulators, The Bounding Gap offers more than a critique of current governance. It identifies the missing boundary and shows why observation, monitoring, alignment, and review cannot substitute for executable refusal.

The future of institutional AI will belong to those who can govern the crossing before machine-generated possibility becomes consequence.

Why Institutions Must Govern Before AI Becomes Consequence

THE BOUNDING GAP

This executive book is a standalone work, not a second edition of Agentic by Design. It is intentionally vendor-neutral and references product names only as examples of categories. It includes no code, configuration, or implementation plan. This book is intended for decision-makers, not implementers.

How AI Governance Became the Growth Engine

Governed Autonomy

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

With Artificial Intelligence (AI) reshaping how businesses operate, the integration of intelligent technologies into Knowledge Management (KM) processes offers new opportunities for optimizing data-driven decision-making and enhancing organizational performance.
AI-Driven Knowledge Management Assets explores KM as a critical element of business strategy and managerial practice, especially in an era of rapid AI adoption. Authors examine KM's foundational and advanced aspects through a managerial lens, highlighting how AI is reshaping contemporary KM practices, and delve into traditional KM strategies and cutting-edge AI applications. Each chapter is enriched with case studies and empirical research that showcase the real-world benefits and challenges of integrating AI into KM. From uncovering the theoretical underpinnings of KM to examining AI-driven innovations that create competitive advantages, this work offers actionable insights and perspectives on future developments. Authors address ethical, sustainability and managerial issues, equipping readers with the tools to navigate the complexities of AI-infused KM practices.
Providing a valuable resource for business leaders, academics, and students, this volume supports those looking to integrate AI into KM to drive strategic decision-making and operational efficiency. Merging traditional knowledge management practices with the latest AI advancements, it prepares readers to harness technology for innovative solutions, positioning their organizations for success in the modern business landscape.

Ai-Driven Knowledge Management Assets

Studies in International Trade and Investment Law

This open access book examines the evolving role of international economic law in addressing two interrelated challenges of the 21st century: enabling digital development and bridging the digital divide. The rapid digitalisation of the economy has generated significant challenges, including a deepening global digital and data divide and a persistent lack of inclusive digital development. Confronting these challenges head on, this pioneering book brings together experts across different legal subfields, geographies, and disciplines to examine the role and relevance of international economic law in bridging the global digital divide and enabling digital development, particularly from the perspective of the Global South. The contributions are anchored in a distinctive analytical framework that traces future directions through which international economic law interacts with digital development, demonstrating how it may function as a constraint, fade into irrelevance, or eventually act as an enabler. The chapters focus on a broad range of areas, including international trade, investment, tax, development, competition, finance and related regulatory and policy domains; they consider multilateral, regional, and plurilateral frameworks; and draw on a plurality of perspectives from Africa, Asia, the Middle East, and Latin America. The authors employ a wide range of methodological approaches to address issues such as digital sovereignty and digital industrial policy, cross-border data flows and digital global value chains, competition in data markets, AI governance, financial inclusion, and the sustainability-digitalisation nexus. This book speaks to scholars, policymakers, and practitioners in international economic law, development studies, and technology law who seek theoretical and pragmatic pathways for reorienting international economic law as a tool for equitable, sustainable, and meaningful digital development. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.

Towards Digital Development

Reorienting International Economic Law for the Digital Economy

Bears & Mosquitoes

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