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|a Q342
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| 100 |
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|a Mishra, Abhishek
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| 245 |
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|a Scalable AI and design patterns
|b design, develop, and deploy scalable AI solutions
|c Abhishek Mishra
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| 260 |
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|a Berkeley, CA
|b Apress L. P.
|c 2024
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| 300 |
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|a xvii, 256 pages
|b illustrations
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| 505 |
0 |
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|a What Is the Importance of Feature Engineering in Scalable AI? -- Practices and Strategies for Feature Engineering -- Advanced Feature Engineering Techniques -- Data Storage and Management Strategies -- Storage Scalability in Data -- Data Storage Methodologies -- Advanced Methods -- Chapter 4: Scalable AI Algorithms and Models -- What Are Scalable AI Algorithms and Models? -- Unlocking Efficiency Through Distributed Computing and Model Optimization -- Types of Scalable AI Algorithms and Models -- The Future of Scalable AI -- Distributed Training Techniques -- Approaches to Online Learning
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| 505 |
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|a Case Studies -- Understanding Model Parallelism -- Why Model Parallelism Matters for Scalability -- Practices and Strategies for Model Parallelism -- Advanced Techniques for Model Parallelism -- Chapter 5: Scalable AI Infrastructure and Architecture -- The Foundation of Scalable AI -- Building Blocks of Scalable AI Architecture -- Containerization and Orchestration for Scalability -- Microservices Architecture -- Container Orchestration Tools -- Orchestration: Managing Containers at Scale -- Advanced Personalization of Content Recommendation
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| 505 |
0 |
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|a Resource Management for Scalable AI and Auto-Scaling Strategies -- Best Practices for Resource Management -- Auto-Scaling Strategies for Scalable AI -- The Need for Auto-Scaling -- Auto-Scaling Strategies -- Chapter 6: Scalable AI Deployment and Productionization -- Why Is Scalable AI Deployment Important? -- Model Versioning and Deployment Strategies -- Why Is Model Versioning Important? -- Best Practices for Model Versioning -- Deployment Strategies: Serving AI at Scale -- Monitoring and Performance Optimization for Scalable AI
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| 505 |
0 |
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|a Flow Diagram for Distributed Computing in Scalable AI -- Use Cases for Distributed Computing -- Example of Distributed Computing in Action -- Parallel Processing Techniques and Scaling AI Models -- Techniques for Parallel Processing in AI -- Challenges in Parallel Processing -- Scaling AI Models: Making Big AI Work for Everyone -- Why Scaling AI Models Matters -- Techniques for Scaling AI Models -- Chapter 3: Data Engineering for Scalable AI -- Why Is Data Engineering Important for AI? -- Data Ingestion and Preprocessing at Scale -- Case Studies -- Feature Engineering for Scalable AI
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| 505 |
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|a Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Introduction -- Chapter 1: Introduction to Scalable AI Systems -- Understanding Scalability in AI Systems -- Why Scalability Matters in AI -- Key Considerations for Scalable AI Systems -- The Need for Design Patterns in Scalable AI -- Challenges and Considerations in Scalable AI Systems -- Chapter 2: Fundamentals of Scalability in AI -- Why Handling Large Datasets Matters -- Techniques for Handling Large Datasets -- Distributed Computing for Scalability -- Techniques for Distributed Computing
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| 653 |
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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| 653 |
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|a Intelligence informatique
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| 653 |
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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| 653 |
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|a Intelligence artificielle
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| 653 |
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|a artificial intelligence / aat
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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| 653 |
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|a Computational intelligence / http://id.loc.gov/authorities/subjects/sh94004659
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| 653 |
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|a Exploration de données (Informatique)
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Description based upon print version of record
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| 028 |
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|a 10.1007/979-8-8688-0158-7
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| 776 |
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|z 9798868801587
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| 776 |
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|z 9798868801570
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9798868801587/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will Learn Develop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processing Know the significance of design patterns in creating robust intelligent solutions Understand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systems Be aware of the ethical implications of scalable AI systems Who This Book Is For AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience
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