Architecting Data and Machine Learning Platforms

🔹 全面掌握雲端資料與AI平台設計關鍵🔹 實戰導向，靈活運用AWS、Azure、Google Cloud、Snowflake、Databricks🔹 從基礎到進階，打造符合企業需求的現代化資料平台🔹 強化資料治理，加速AI/ML創新，驅動決策智慧無論你是資料架構師、工程師，或是希望運用AI強化決策的企業領導者，本書都將成為你打造未來資料與機器學習平台的最佳指南！在資料驅動的時代，企業該如何善用資料創造價值？本書由三位資深工程師聯手打造，為雲端架構師與資料專業人士提供了一套清晰完整的解決方案。從雲端資料平台的設計到AI與機器學習的整合，帶您走過資料現代化的每一步。無論是整合分散的資料、實現即時決策，還是利用AI解鎖創新潛力，本書都提供了實用的架構與工具，幫助你在雲端時代保持優勢。-----------------------------------------------------------「本書完整介紹，如何依據企業組織的策略方向，設計與建置現代雲端資料與ML平台的概念、模式與元件。真希望我能在多年前就能讀到這本書。」—Robert Sahlin，Mathem資料平台主管所有的雲端架構師都必須知道如何建置資料平台，以賦與企業能夠快速高效地做出資料驅動的決策，並在整個企業範圍內提供智能化服務。本書將展示如何使用AWS、Azure、Google Cloud以及Snowflake與Databricks這類多重雲端工具，設計、建置與現代化雲端原生資料與機器學習平台。作者Marco Tranquillin、Valliappa Lakshmanan與Firat Tekiner使用真實世界企業架構來說明，內容涵蓋從雲端匯入到啟動整個資料生命週期。你將學到如何轉換、保全與現代化資料倉儲與資料湖這些熟悉的解決方案，也將能充份利用最新的AI/ML模式取得精準且快速的洞見，提升競爭優勢。你將學會：*設計現代化且安全的雲端原生或混合資料分析與機器學習平台*透過將企業資料整合到治理良好、可擴充的與彈性的資料平台，加速資料導向的創新。*民主化存取企業資料，治理業務團隊提取洞見方式與建置AI/ML的能力。*讓您的企業利用串流管道即時決策*建置MLOps平台，移往預測性與指示性分析

Core Insight：Cross-Domain Data Collaboration: Unlocking Chip PotentialEliminating the "efficiency gap" caused by knowledge silos is the starting point for industry potential. Data doesn't lie; Data Science provides the solution: Through innovation and cross-domain restructuring, we visualize results to become the critical accelerator for boosting efficiency.GenAI Reshaping the Future of SemiconductorsFacing the immense challenge of the slowing pace of Moore's Law, the semiconductor industry urgently requires new breakthroughs. This book is specifically designed to solve the "Efficiency Black Hole" that consumes tens of billions of dollars annually in the industry.The Empowering DTCO Innovation with AI and Machine Learning offers readers a practical DTCO.ML Framework, demonstrating how to leverage Machine Learning (ML) and Generative AI (GenAI) technologies to inject new acceleration into chip manufacturing processes. Learn to master process variability and optimize chip energy efficien

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, th

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo

Modern statistical methods allow the analyst to fit and assess models as well as to undertake supervised or unsupervised learning to extract information. Contemporary data science requires tight integ

Using a unique multimedia format for learning the fundamentals of data structures and algorithms, this conceptually elegant and innovative text incorporates the object-oriented design paradigm with Ja

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investmentBig Data and Machine Learning in Quantitative Investment is not just about demonstrat

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in r

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques in real-world dat

In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive

Learning to spell really can be fun, especially when you have an amazing Spelling Machine, a 2-book series for preschoolers ages 4-6 to learn spelling and start to read. Within each scene there are five objects to find and learn to spell. Flip out your magical spelling machine contained inside the book, dial in the letters of each word and lift the flap to check your answer!※無音檔英文學習遊戲書有了spelling machine的神奇轉盤，拼單字，也可以變得超級有趣！全新包裝，內容再升級知名親子叢書作家廖彩杏推薦：“ Spelling Machine：Make Spelling Fun！”是一本寓教於樂的遊戲書，讓孩子從小小的拼字遊戲，得到樂趣。本套書以「度假」為主題，分6大場景，共收錄30個常用英文單字。在不同場景，小朋友要先動手動眼，找一找不同物件在哪裡？並學會認識這些物件的英文，學會認字後，用前方的轉盤練習拼字。剛開始認字、學習自然發音的小讀者，都能從遊戲中開心學習！ 【玩法說明】1.第一步，先把紅圈順時針轉到「start」處。2.假設今天要讓孩子熟悉crab的拼法，就先大聲念出crab，讓孩子聽聽看，這個單字，是由哪些字母所組成的。3.現在要拼出crab，就把第一個字母c，順時針轉到start處。4.再把第二個字母r，撥到start處，接下來的字母以此類推。5.最後.恭喜，完成了！請孩子打開翻頁機關確認答案，看到「crab」了嗎？ 是不是好像變魔術一樣，好神奇呢？拼字，也可以這麼有趣！

Focuses on a new topic each week covering map skills, applying information and data, and connecting what was learnedBuilds student's confidence in geography with quick independent learning activities

Focuses on a new topic each week covering map skills, applying information and data, and connecting what was learnedBuilds student's confidence in geography with quick independent learning activities

Focuses on a new topic each week covering map skills, applying information and data, and connecting what was learnedBuilds student's confidence in geography with quick independent learning activities

Focuses on a new topic each week covering map skills, applying information and data, and connecting what was learnedBuilds student's confidence in geography with quick independent learning activities

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main conce

In an era where many believe Big Data has rendered human perception and observation 'old-school' or pass', Martin Lindstrom shows that mining and matching technological data with up-close psychologica

Proven strategies for implementing the right data warehouse and business intelligence solutions for current and future business needs In The Data Warehouse Mentor: Practical Data Warehouse and Busines

Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on such topics as GANs, transformers, BERT, GPT, VAE, adversarial examples, pre-training and fine-tuning, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with ne

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.Today, machine learning underlies a range of app

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure.Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

The first machine-learning guide that helps you understand customers, frame decisions, and drive value Business Data Science reveals the best ways for utilizing machine learning (ML) to mak

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The

3 comprehensive manuscripts in 1 bookMachine Learning: An Essential Guide to Machine Learning for Beginners Who Want to Understand Applications, Artificial Intelligence, Data Mining, Big Data and More

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning,

Machine learning applications autonomously reason about data at massive scale. It's important that they remain responsive in the face of failure and changes in load. But machine learning systems are d

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random for

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deployin

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and jour

With an increasing concern of personal data privacy in Hong Kong and around the world, the Office of the Privacy Commissioner for Personal Data (PCPD), Hong Kong publishes this third edition of Personal Data (Privacy) Law in Hong Kong: A Practical Guide on Compliance to provide practical guidance on the law of privacy, and the ways to protect personal data in everyday life.This third edition will guide readers through the recent developments of personal data privacy in the local, regional and global contexts with updates on recent Administrative Appeals Board and Court decisions, and investigation reports and materials from the PCPD. In addition, some most-concerned issues are discussed, such as ethical development and use of AI, the collection and use of personal data by employers during COVID-19, data protection under work-from-home arrangements, data security measures for ICT systems, guidelines on doxxing offences and data breach handling. This book will certainly provide readers a

This book illustrates and explains the key concepts of business data analytics from scratch, tackling the day-to-day challenges of a business data analyst. It provides you with all the professional tools you need to predict online shop sales, to conduct A/B tests on marketing campaigns, to generate automated reports with PowerPoint, to extract datasets from Wikipedia, and to create interactive analytics Web apps. Alongside these practical projects, this book provides hands-on coding exercises, case studies, the essential programming tools and the CRISP-DM framework which you'll need to kickstart your career in business data analytics.The different chapters prioritize practical understanding over mathematical theory, using realistic business data and challenges of the Junglivet Whisky Company to intuitively grasp key concepts and ideas. Designed for beginners and intermediates, this book guides you from business data analytics fundamentals to advanced techniques, covering a large number