Nearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer.
You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. 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.

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Spis treści

Google Machine Learning and Generative AI for Solutions Architects. ​Build efficient and scalable AI/ML solutions on Google Cloud eBook -- spis treÅ›ci

• 1. AI/ML Concepts, Real-World Applications, and Challenges
• 2. Understanding the ML Model Development Lifecycle
• 3. AI/ML Tooling and the Google Cloud AI/ML Landscape
• 4. Utilizing Google Cloud's High-Level AI Services
• 5. Building Custom ML Models on Google Cloud
• 6. Diving Deeper—Preparing and Processing Data for AI/ML Workloads on Google Cloud
• 7. Feature Engineering and Dimensionality Reduction
• 8. Hyperparameters and Optimization
• 9. Neural Networks and Deep Learning
• 10. Deploying, Monitoring, and Scaling in Production
• 11. Machine Learning Engineering and MLOps with GCP
• 12. Bias, Explainability, Fairness, and Lineage
• 13. ML Governance and the Google Cloud Architecture Framework
• 14. Advanced Use Cases and Technologies
• 15. An Introduction to Generative AI
• 16. Generative AI on Google Cloud
• 17. Advanced Generative AI Concepts and Use Cases
• 18. Bringing It All Together—Building ML Solutions with GCP and Vertex