دانلود کتاب راهنمای بهینه‌سازی هوش مصنوعی: موفقیت کسب و کار را با استراتژی‌های اثبات‌شده هوش مصنوعی، بهترین شیوه‌ها و نوآوری مسئولانه پیش ببرید

The AI Optimization Playbook aims to train business leaders, data scientists, and AI engineers to achieve transformative business value throughout the entire AI project lifecycle. It covers not only the technical aspect of model optimization but also the crucial elements of strategy, leadership, project selection, AI/MLOps, and responsible AI governance required for puing AI into production successfully. The book introduces several key frameworks, such as the AI project lifecycle (from strategy to prototype to production to iteration), to provide proven approaches for consistently producing high- quality, high-impact output. This book guides you on how to formulate an AI strategy, select high-impact projects, and navigate the entire lifecycle from pilot to production. It also details how to operationalize these systems using MLOps and, more recently, LLMOps for generative AI, to ensure solutions are scalable and reliable. Taking a practical, hands-on approach using real-world case studies, the book also dives deeper into the concepts behind why AI projects fail. This ensures that you will not only learn how to build something but also understand why strategic alignment and responsible governance are critical for producing scalable business outcomes. Learning to effectively manage this entire lifecycle is an empowering skill set in the quickly evolving field of artificial intelligence. The book is structured into five parts: Part 1 , Laying the Groundwork for AI Success , establishes the strategic foundation necessary for any successful AI initiative. We will begin by examining why most AI projects fail, showing that these are overwhelmingly strategy, execution, and integration failures, not technology failures. We will analyze common pitfalls, such as misaligned goals (chasing technical “surrogate metrics” instead of business value), siloed development, weak data foundations, and a misunderstanding of AI’s iterative and non-deterministic nature. Having established what goes wrong, this part translates these lessons into a practical framework for building a mature AI strategy. This includes using evaluation processes such as the ICE framework to prioritize a project roadmap, building robust governance and compliance using established models such as the NIST AI RMF, and designing a modern data strategy that treats data as a product. Finally, we cover the need for a scalable AI platform, a principled algorithm selection strategy, and a hybrid “hub-and-spoke” organizational structure to manage change. Part 2 , Aligning Projects with Business Impact , provides a practical blueprint for bridging the gap between strategy and execution, beginning with the selection of high-impact AI projects. We will first detail how to analyze and prioritize initiatives, moving beyond just technical novelty to focus on factors such as business impact, end-user definition, and comprehensive feasibility analysis covering data, the tech stack, and talent. This includes quantitative methods for opportunity sizing, such as T-shirt sizing and boom-up comparables, as well as rigorous cost-benefit analysis to justify the potential ROI. Once a project is identified, we will cover the critical next step of gaining leadership support, offering a guide to crafting a compelling narrative by engaging stakeholders early, focusing on the “so-what” of business value, presenting a phased roadmap, and seing realistic expectations. With sponsorship secured, we will present a playbook for building an effective Proof of Concept ( PoC ) as a small-scale, low-risk experiment to test feasibility, detailing the critical post-PoC decisions: whether to refine, pivot, or proceed to an MVP. Finally, we will introduce a comprehensive framework for measuring the PoC’s performance beyond just technical model metrics, covering the essential system, business, and safety metrics needed to prove its value and secure a clear path to production. Part 3 , Deploying and Proving ML Value , provides a comprehensive guide to the complete, end-to-end lifecycle of an ML system, moving from defining its purpose to proving its causal impact on the business. We will start with the foundational framework for success, moving beyond simple accuracy to define multi- dimensional, business-aligned goals, essential guardrail metrics to prevent unintended harm, and surrogate metrics to track long-term objectives. Next, we will dive into “productization,” which is the critical process of operationalizing models from experimental notebooks into robust, production-grade systems, exploring MLOps best practices, the importance of reproducible pipelines for code and data, and the architectural choices for model serving. Finally, we will explore the science of causal inference to answer the critical question, “Did the system do what it was intended to do?”, covering the “gold standard” of A/B testing (RCTs), as well as advanced observational techniques and optimization methods such as multi-arm bandits. Part 4 , Emerging Topics: Generative AI and AI Agents , explores the new frontier of AI technologies that are transforming enterprises. We will begin by explaining generative AI and LLMs in business-friendly terms, covering key use cases across various functions, from marketing and customer service to code generation, as well as practical adoption considerations. Following this, we will define LLMOps and detail how it extends traditional MLOps to handle the unique operational lifecycle of large language models, including prompt orchestration, fine-tuning, and specialized monitoring. Finally, we will explore the emerging world of AI agents, discussing what they are, their potential to automate complex multi-step tasks, and the frameworks for building and governing these autonomous systems safely. Part 5 , Responsible AI and Governance , provides a crucial framework for ensuring AI solutions are ethical, fair, compliant, and sustainable, starting by defining Responsible AI ( RAI ) and its core pillars (FEAT). It then provides a practical guide to operationalizing these principles through governance commiees, risk assessment checklists (Risk Score = Likelihood * Impact), and strategic Human-in- the-Loop ( HITL ) approaches. The part also addresses the unique challenges of trustworthy LLMs, such as “hallucinations,” and surveys the evolving global regulatory landscape, including the EU AI Act, before concluding with a look at future trends such as quantum computing and a vision of the fully optimized, AI- driven enterprise of 2030.