{"title":"AI \u0026 ML","description":"","products":[{"product_id":"fundamentals-of-data-engineering-1st-edition","title":"Fundamentals of Data Engineering","description":"\u003cdiv data-is-intersecting=\"true\" data-turn-id-container=\"request-WEB:9f9d05cb-792c-495d-aa42-29a847dd04bc-0\" class=\"\"\u003e\n\u003cdiv class=\"relative w-full overflow-visible\"\u003e\n\u003csection data-turn=\"assistant\" data-scroll-anchor=\"false\" data-testid=\"conversation-turn-2\" data-turn-id-container=\"request-WEB:9f9d05cb-792c-495d-aa42-29a847dd04bc-0\" data-turn-id=\"request-WEB:9f9d05cb-792c-495d-aa42-29a847dd04bc-0\" dir=\"auto\" class=\"text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [\u0026amp;:has([data-writing-block])\u0026gt;*]:pointer-events-auto R6Vx5W_threadScrollVars scroll-mb-[calc(var(--scroll-root-safe-area-inset-bottom,0px)+var(--thread-response-height))] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\"\u003e\n\u003cdiv class=\"text-base my-auto mx-auto [--thread-content-margin:var(--thread-content-margin-xs,calc(var(--spacing)*4))] @w-sm\/main:[--thread-content-margin:var(--thread-content-margin-sm,calc(var(--spacing)*6))] @w-lg\/main:[--thread-content-margin:var(--thread-content-margin-lg,calc(var(--spacing)*16))] px-(--thread-content-margin)\"\u003e\n\u003cdiv class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\"\u003e\n\u003cdiv class=\"flex max-w-full flex-col gap-4 grow\"\u003e\n\u003cdiv class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal outline-none keyboard-focused:focus-ring [.text-message+\u0026amp;]:mt-1\" data-message-model-slug=\"gpt-5-5\" dir=\"auto\" data-message-id=\"1f9ffb05-578a-47c1-bb50-332b808d5766\" data-message-author-role=\"assistant\"\u003e\n\u003cdiv class=\"flex w-full flex-col gap-1 empty:hidden\"\u003e\n\u003cdiv class=\"markdown prose dark:prose-invert wrap-break-word w-full dark markdown-new-styling\"\u003e\n\u003cstrong\u003eDetailed Overview\u003c\/strong\u003e:\u003cbr\u003e\u003cbr\u003e\u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eFundamentals of\u003cstrong\u003e Data Engineering\u003c\/strong\u003e\u003c\/span\u003e\u003c\/span\u003e\u003cstrong\u003e is a comprehensive guide for modern data engineers, software developers, data analysts, and technology professionals\u003c\/strong\u003e who want to understand how robust data systems are designed and managed in real-world organizations. The book explains the complete data engineering lifecycle, including data generation, ingestion, storage, transformation, orchestration, governance, security, and serving data to downstream applications and users.\n\u003cp data-end=\"839\" data-start=\"494\"\u003e\u003cstrong\u003eRather than focusing only on tools and technologies, the authors emphasize practical architecture design, business requirements, scalability, and operational best practices.\u003c\/strong\u003e The book helps readers understand how to evaluate modern cloud-based technologies and avoid unnecessary complexity while building reliable and future-ready data platforms.\u003c\/p\u003e\n\u003cp data-end=\"1196\" data-start=\"841\"\u003eIt is widely recommended for professionals working with \u003cstrong\u003ebig data, analytics, cloud infrastructure, machine learning pipelines, and enterprise data systems.\u003c\/strong\u003e The content combines technical depth with practical insights, making it suitable for both beginners and experienced professionals in the data engineering domain.\u003cbr\u003e\u003cstrong\u003e\u003cspan style=\"font-family: -apple-system, BlinkMacSystemFont, 'San Francisco', 'Segoe UI', Roboto, 'Helvetica Neue', sans-serif; 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Written by one of the leading voices in modern AI infrastructure and machine learning systems, this book bridges the gap between theoretical machine learning knowledge and real-world AI deployment.\u003c\/p\u003e\n\u003cp data-start=\"448\" data-end=\"855\"\u003eThe book focuses on the engineering challenges involved in developing AI-powered applications, including data pipelines, model training, evaluation, deployment, monitoring, optimization, and system scalability. Chip Huyen explains complex AI engineering concepts in a clear and accessible manner, helping readers understand how to design robust machine learning workflows for modern production environments.\u003c\/p\u003e\n\u003cp data-start=\"857\" data-end=\"1274\"\u003eCovering topics such as foundation models, MLOps, inference optimization, vector databases, retrieval-augmented generation (RAG), AI infrastructure, and responsible AI practices, the book reflects the latest advancements in applied artificial intelligence. Real-world examples, engineering insights, and practical implementation strategies make this resource valuable for both beginners and experienced practitioners.\u003c\/p\u003e\n\u003cp data-start=\"1276\" data-end=\"1541\"\u003eIdeal for software engineers, machine learning engineers, data scientists, AI researchers, and technology professionals, \u003cem data-start=\"1397\" data-end=\"1413\"\u003eAI Engineering\u003c\/em\u003e serves as an essential reference for anyone building and deploying AI systems in today’s rapidly evolving technology landscape.\u003c\/p\u003e\n\u003cp data-start=\"1276\" data-end=\"1541\"\u003e\u003cimg alt=\"\" src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0761\/2206\/3013\/files\/e40ea92c-0ed8-480c-b851-cc4fd779d37f.png?v=1779099749\"\u003e\u003c\/p\u003e\n\u003cstrong\u003eProduct Details\u003c\/strong\u003e:The Essential Guide to Modern AI Engineering\n\u003cp data-start=\"3193\" data-end=\"3418\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003cstrong\u003eTitle\u003c\/strong\u003e: AI Engineering: 1st Edition\u003cbr data-start=\"3227\" data-end=\"3230\"\u003e\u003cstrong\u003eAuthor\u003c\/strong\u003e: Chip Huyen\u003cbr data-start=\"3248\" data-end=\"3251\"\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 978-1098166304\u003cbr data-start=\"3274\" data-end=\"3277\"\u003e\u003cstrong\u003eISBN-10\u003c\/strong\u003e: 1098166302\u003cbr data-start=\"3296\" data-end=\"3299\"\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: O'Reilly Media\u003cbr data-start=\"3324\" data-end=\"3327\"\u003e\u003cstrong\u003eBinding\u003c\/strong\u003e: Paperback\u003cbr data-start=\"3345\" data-end=\"3348\"\u003e\u003cstrong\u003eNo of Pages\u003c\/strong\u003e: 386\u003cbr data-start=\"3364\" data-end=\"3367\"\u003e\u003cstrong\u003eLanguage\u003c\/strong\u003e: English\u003cbr data-start=\"3384\" data-end=\"3387\"\u003e\u003cstrong\u003ePublisher Date\u003c\/strong\u003e: October 8, 2024\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"mt-3 w-full empty:hidden\"\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"The Books Empire","offers":[{"title":"Default Title","offer_id":47824635068581,"sku":null,"price":36.9,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0761\/2206\/3013\/files\/815KH9GjFTL._SL1500.jpg?v=1778675170"},{"product_id":"designing-data-intensive-applications-1st-edition","title":"Designing Data-Intensive Applications","description":"\u003ch4 data-end=\"21\" data-start=\"0\" data-section-id=\"da8d7w\"\u003eDetailed Overview:\u003c\/h4\u003e\n\u003cp data-end=\"396\" data-start=\"23\"\u003e\u003cem data-end=\"62\" data-start=\"23\"\u003eDesigning Data-Intensive Applications\u003c\/em\u003e by Martin Kleppmann is an essential guide for software engineers, backend developers, and system architects who want to understand the principles behind scalable and reliable modern applications. 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It helps readers understand the trade-offs involved in designing distributed architectures and selecting the right technologies for different workloads.\u003c\/p\u003e\n\u003cp data-end=\"1276\" data-start=\"1036\"\u003eIdeal for developers, DevOps engineers, data engineers, and technology professionals, this book serves as both a learning resource and a long-term reference for building maintainable and scalable software systems in real-world environments.\u003c\/p\u003e\n\u003cdiv style=\"text-align: left;\"\u003e\n\u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0761\/2206\/3013\/files\/Screenshot_2026-05-16_at_1.11.21_PM.png?v=1778917356\" alt=\"\"\u003e\u003cstrong\u003e\u003cbr\u003e\u003c\/strong\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"text-align: left;\"\u003e\n\u003cstrong\u003e\u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0761\/2206\/3013\/files\/Screenshot_2026-05-16_at_1.11.56_PM.png?v=1778917356\" alt=\"\"\u003e\u003cbr\u003eProduct Details:\u003c\/strong\u003e\u003cbr\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003eTitle: \u003c\/strong\u003eDesigning Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003eAuthor: \u003c\/strong\u003eMartin Kleppmann\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003eISBN-13: \u003c\/strong\u003e 9781449373320\u003cbr\u003e\u003c\/span\u003e\u003cspan style=\"font-family: -apple-system, BlinkMacSystemFont, 'San Francisco', 'Segoe UI', Roboto, 'Helvetica Neue', sans-serif; font-size: 0.875rem;\"\u003e\u003cstrong\u003eISBN-10: \u003c\/strong\u003e1449373321\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e O'Reilly Media\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003eBinding: \u003c\/strong\u003ePaperback\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003eNo of Pages:\u003c\/strong\u003e 611\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"_aupe copyable-text xkrh14z\"\u003e\u003cstrong\u003eLanguage:\u003c\/strong\u003e English\u003cbr\u003e\u003c\/span\u003e\u003cspan style=\"font-family: -apple-system, BlinkMacSystemFont, 'San Francisco', 'Segoe UI', Roboto, 'Helvetica Neue', sans-serif; font-size: 0.875rem;\"\u003e\u003cstrong\u003ePublisher Date:\u003c\/strong\u003e 02 May 2017\u003c\/span\u003e\n\u003cp data-end=\"1276\" data-start=\"1036\"\u003e \u003c\/p\u003e\n\u003c\/div\u003e","brand":"The Books Empire","offers":[{"title":"Default Title","offer_id":47824643522725,"sku":null,"price":39.9,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0761\/2206\/3013\/files\/91YfNb49PLL._SL1500.jpg?v=1778676167"},{"product_id":"data-science-from-scratch-2nd-edition","title":"Data Science from Scratch: 2nd Edition","description":"\u003ch4 data-section-id=\"da8d7w\" data-start=\"0\" data-end=\"21\"\u003eDetailed Overview:\u003c\/h4\u003e\n\u003cp data-start=\"23\" data-end=\"614\"\u003e\u003cstrong\u003e\u003cem data-start=\"23\" data-end=\"93\"\u003eData Science from Scratch: First Principles with Python, 2nd Edition\u003c\/em\u003e by Joel Grus\u003c\/strong\u003e is a practical and accessible guide designed for readers who want to understand the foundations of data science through hands-on Python programming. Rather than relying heavily on complex libraries or black-box tools, the book explains how core data science concepts work from the ground up. It introduces essential topics such as statistics, probability, linear algebra, machine learning, data visualization, natural language processing, and deep learning using clear explanations and real coding examples.\u003c\/p\u003e\n\u003cp data-start=\"616\" data-end=\"1112\"\u003eThe second edition has been updated to reflect modern Python practices and current data science techniques, making it highly valuable for beginners, programmers, analysts, and aspiring machine learning professionals. Readers learn how to build algorithms step by step, gaining a deeper understanding of how models and analytical methods function internally. 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The book focuses on the powerful Hugging Face ecosystem and demonstrates how to build state-of-the-art NLP applications with modern deep learning techniques. Covering both foundational concepts and hands-on implementation, it helps readers understand how transformers have revolutionized tasks such as text classification, question answering, summarization, translation, and conversational AI.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe revised edition includes updated workflows, improved model deployment strategies, and the latest transformer architectures used in real-world AI systems. Readers learn how to fine-tune pretrained language models, work with datasets, optimize training pipelines, and deploy NLP applications efficiently. 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