Google Machine Learning and Generative AI for Solutions Architects
Google Machine Learning and Generative AI for Solutions Architects
Build efficient and scalable AI/ML solutions on Google Cloud
About the Book
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.
About the Author
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
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