{"product_id":"advanced-customized-artificial-intelligence-fundamentals-with-python-domain-adaptation-personalization-and-deployment-9798248357710","title":"Advanced Customized Artificial Intelligence Fundamentals With Python: Domain Adaptation, Personalization, and Deployment","description":"\u003cp\u003e • Author(s): J. Melbokzo\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Artificial Intelligence - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis is a rigorous, idea-dense guide to building AI systems that are not merely accurate, but intentionally shaped for specific users, tenants, and domains. Instead of treating customization as a cosmetic layer on top of a general model, it reframes customization as a deep engineering and scientific discipline: negotiating objective functions, drawing data boundaries, controlling semantic drift, designing retrieval as governance, and turning evaluation into falsifiable stress tests.\u003c\/p\u003e\u003cp\u003eAcross 24 tightly focused chapters, you will learn how to make advanced AI behave predictably under real constraints: conflicting policies, multi-tenant privacy, adversarial inputs, shifting regulations, and evolving organizational incentives. The emphasis is on non-obvious tradeoffs that experienced practitioners recognize as the difference between a demo and a deployable system: how routing errors silently dominate overall risk, why prompt and context management is an attention economics problem, when parameter-efficient fine-tuning outperforms full retraining, and how continual learning can be engineered without institutional amnesia.\u003c\/p\u003e\u003cp\u003eEvery chapter includes full Python code demos that translate the conceptual frameworks into reproducible experiments, evaluation harnesses, and deployment-ready patterns. You will build tenant-aware testing suites, implement calibrated uncertainty and abstention, harden tool-using agents against prompt injection, construct provenance and versioning mechanisms for behavior, and design hybrid neural plus rule systems that enforce correctness when correctness is non-negotiable.\u003c\/p\u003e\u003cp\u003eThe result is a modern, sophisticated approach to AI fundamentals that treats customization as the core problem, not a final step. If you want principled methods for controlling behavior at scale, aligning systems to domain-specific obligations, and defending reliability in the presence of drift, incentives, and attack surfaces, this book provides the mental models and executable workflows to do it.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47569546903703,"sku":"9798248357710","price":6587.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798248357710.webp?v=1774878017","url":"https:\/\/atlanticbooks.com\/products\/advanced-customized-artificial-intelligence-fundamentals-with-python-domain-adaptation-personalization-and-deployment-9798248357710","provider":"Atlantic Books","version":"1.0","type":"link"}