What Every Engineer Should Know about Artificial Intelligence and Big Data (What Every Engineer Should Know)

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予約

What Every Engineer Should Know about Artificial Intelligence and Big Data (What Every Engineer Should Know)

Srinivasan, Satish Mahadevan/ Sangwan, Raghvinder S.

ウェブストア価格 ¥14,996（本体¥13,633）
CRC Press（2026/07発売）
外貨定価 US$ 66.99
ポイント 136pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 280 p.
言語 ENG
商品コード 9781032829852

Full Description

Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging towards the adoption of distributed open-source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. Rather than focusing on theory, the book shares real-life experiences building AI and big data analytics systems of value to practitioners.

• Features practical case studies on building big data and AI models for large scale enterprise solutions.

• Discusses the use of design patterns for architecting AI that are safe, secure, and testable.

• Covers an array of concepts including deep big data analytics, natural language processing, transformer architecture and evolution of ChatGPT, swarm intelligence, and genetic programming.

Informed by the authors' many years of teaching ML, AI, and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.

0. Front Matter. Part I. Foundations & Platforms, Automation & Data Quality at Scale. 1. Fundamental concepts in AI. 2. Big Data and Artificial Intelligence Systems. 3. Architecting Big Data pipelines. 4. Big Data Frameworks and Data Cleaning Strategies. 5. Building Automated Pipelines for Data Cleaning. Part II. Optimization & Search. 6. Swarm Intelligence. 7. Genetic Programming. Part III. Learning Systems. 8. Foundations on Machine Learning and Artificial Learning. 9. Reinforcement Learning. 10. Deep Reinforcement Learning. 11. Natural Language Modelling. 12. Transformer Architecture and Evolution of LLM's. Part IV. Systems in the Real World. 13. Architecting Distributed AI Systems using Design Patterns. 14. Securing AI Systems. 15. AI System Safety in Practice. 16. Testing Strategies for AI Applications. End Matter. Answer Keys for Chapter Questions.