Kavanagh Google Machine Learning and Generative AI for Solutions Architects

Build efficient and scalable AI/ML solutions on Google Cloud
1. Auflage 2024
ISBN: 978-1-80324-702-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 552 Seiten

Most companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world's leading tech companies.
You'll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today's market. You'll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You'll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.
By the end of this book, you will be able to unlock the full potential of Google Cloud's AI/ML offerings.

Autoren/Hrsg.

Kavanagh, Kieran

Weitere Mitwirkende

Vergadia, Priyanka

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - AI/ML Concepts, Real-World Applications, and Challenges

- Understanding the ML Model Development Lifecycle
- AI/ML Tooling and the Google Cloud AI/ML Landscape
- Utilizing Google Cloud's High-Level AI Services
- Building Custom ML Models on Google Cloud
- Diving Deeper—Preparing and Processing Data for AI/ML Workloads on Google Cloud
- Feature Engineering and Dimensionality Reduction
- Hyperparameters and Optimization
- Neural Networks and Deep Learning

- Deploying, Monitoring, and Scaling in Production
- Machine Learning Engineering and MLOps with GCP
- Bias, Explainability, Fairness, and Lineage
- ML Governance and the Google Cloud Architecture Framework
- Advanced Use Cases and Technologies
- An Introduction to Generative AI
- Generative AI on Google Cloud
- Advanced Generative AI Concepts and Use Cases
- Bringing It All Together—Building ML Solutions with GCP and Vertex

Über Autor(innen)

Kavanagh Kieran :

Kieran Kavanagh is a Principal Architect at Google Cloud, working with Google's largest retail customers and driving some of the industry's most challenging digital transformation and generative AI initiatives. Before joining Google, he was a Principal AI/ML Solutions Architect in Strategic Accounts at Amazon Web Services (AWS), building some of the most complex AI/ML systems in the world. He was also a Principal Architect at AT&T, leading their Mobile Internet infrastructure design, and he is a public speaker on the topics of AI/ML, MLOps, and large-scale cloud transformation. Originally from Cork, Ireland, he now lives in Atlanta, GA, with his wife, Katelyn.