Scalable AI and Design Patterns

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

Explains scalable AI and design patterns, including the challenges of handling large data sets, and real-time processing Provides insights into scalable algorithms, models, infrastructure, and architecture considerations Explores the potential of edge computing for AI scalability

Auteur

Abhishek Mishra is a seasoned data science and analytics professional with extensive expertise in information technology services and products. With a remarkable 15-year track record, Abhishek has leveraged his data science knowledge and engineering skills to analyze vast datasets, interpret findings, and communicate actionable insights to drive effective business decision making. He has successfully led global analytics teams, developed cutting-edge machine learning solutions, and formulated data analytics strategies for esteemed organizations such as Marsh and McLennan, Fiserv India Pvt Ltd, and Deloitte Inc.

Contenu

Chapter 1: Introduction to Scalable AI Systems.- Chapter 2: Fundamentals of Scalability in AI.- Chapter 3: Data Engineering for Scalable AI.- Chapter 4: Scalable AI Algorithms and Models.- Chapter 5: Scalable AI Infrastructure and Architecture.- Chapter 6: Scalable AI Deployment and Productionization.- Chapter 7: Scalable AI for Real-time and Streaming Data.- Chapter 8: Scalable AI for Edge Computing.- Chapter 9: Scalable AI Governance and Ethics.- Chapter 10: Case Studies and Best Practices.- Chapter 11: Future Trends and Emerging Technologies.- Chapter 12: Conclusion and Final Thoughts.