Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.
Pagabile anche con Carta della cultura giovani e del merito, 18App Bonus Cultura e Carta del Docente
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
Who This Book Is For
AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience
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.
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.
Il sito utilizza cookie ed altri strumenti di tracciamento che raccolgono informazioni dal dispositivo dell’utente. Oltre ai cookie tecnici ed analitici aggregati, strettamente necessari per il funzionamento di questo sito web, previo consenso dell’utente possono essere installati cookie di profilazione e marketing e cookie dei social media. Cliccando su “Accetto tutti i cookie” saranno attivate tutte le categorie di cookie. Per accettare solo deterninate categorie di cookie, cliccare invece su “Impostazioni cookie”. Chiudendo il banner o continuando a navigare saranno installati solo cookie tecnici. Per maggiori dettagli, consultare la Cookie Policy.