This document in the Google Cloud Architecture Framework describes principles and recommendations to help you to design, build, and manage AI and ML workloads in Google Cloud that meet your operational, security, reliability, cost, and performance goals.
The target audience for this document includes decision makers, architects, administrators, developers, and operators who design, build, deploy, and maintain AI and ML workloads in Google Cloud.
The following pages describe principles and recommendations that are specific to AI and ML, for each pillar of the Google Cloud Architecture Framework:
- AI and ML perspective: Operational excellence
- AI and ML perspective: Security
- AI and ML perspective: Reliability
- AI and ML perspective: Cost optimization
- AI and ML perspective: Performance optimization
Contributors
Authors:
- Benjamin Sadik | AI and ML Specialist Customer Engineer
- Filipe Gracio, PhD | Customer Engineer
- Isaac Lo | AI Business Development Manager
- Kamilla Kurta | GenAI/ML Specialist Customer Engineer
- Mohamed Fawzi | Benelux Security and Compliance Lead
- Rick (Rugui) Chen | AI Infrastructure Solutions Architect
- Sannya Dang | AI Solution Architect
Other contributors:
- Daniel Lees | Cloud Security Architect
- Gary Harmson | Customer Engineer
- Jose Andrade | Enterprise Infrastructure Customer Engineer
- Kumar Dhanagopal | Cross-Product Solution Developer
- Marwan Al Shawi | Partner Customer Engineer
- Nicolas Pintaux | Customer Engineer, Application Modernization Specialist
- Radhika Kanakam | Senior Program Manager, Cloud GTM
- Ryan Cox | Principal Architect
- Stef Ruinard | Generative AI Field Solutions Architect
- Wade Holmes | Global Solutions Director
- Zach Seils | Networking Specialist