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What's new
Fundamentals
Content overview
Well-Architected Framework
Overview
What's new
Pillars
Operational excellence
Overview
Ensure operational readiness and performance using CloudOps
Manage incidents and problems
Manage and optimize cloud resources
Automate and manage change
Continuously improve and innovate
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Security, privacy, and compliance
Overview
Implement security by design
Implement zero trust
Implement shift-left security
Implement preemptive cyber defense
Use AI securely and responsibly
Use AI for security
Meet regulatory, compliance, and privacy needs
Shared responsibility and shared fate
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Reliability
Overview
Define reliability based on user-experience goals
Set realistic targets for reliability
Build high availability through redundancy
Take advantage of horizontal scalability
Detect potential failures by using observability
Design for graceful degradation
Perform testing for recovery from failures
Perform testing for recovery from data loss
Conduct thorough postmortems
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Cost optimization
Overview
Align spending with business value
Foster a culture of cost awareness
Optimize resource usage
Optimize continuously
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Performance optimization
Overview
Plan resource allocation
Take advantage of elasticity
Promote modular design
Continuously monitor and improve performance
View on one page
Sustainability
Overview
Use low-carbon regions
Optimize AI and ML workloads
Optimize resource usage
Develop energy-efficient software
Optimize data and storage
Continuously measure and improve
Promote a culture of sustainability
Align with industry guidelines
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View all the pillars on one page
Cross-pillar perspectives
AI and ML
Overview
Operational excellence
Security
Reliability
Cost optimization
Performance optimization
View on one page
Financial services (FS)
Overview
Operational excellence
Security
Reliability
Cost optimization
Performance optimization
View on one page
Deployment archetypes
Overview
Zonal
Regional
Multi-regional
Global
Hybrid
Multicloud
Comparative analysis
What's next
Reference architectures
Single-zone deployment on Compute Engine
Regional deployment on Compute Engine
Multi-regional deployment on Compute Engine
Global deployment on Compute Engine and Spanner
Landing zone design
Landing zones overview
Decide identity onboarding
Decide resource hierarchy
Network design
Decide network design
Implement network design
Decide security
Enterprise foundations blueprint
Overview
Architecture
Authentication and authorization
Organization structure
Networking
Detective controls
Preventative controls
Deployment methodology
Operations best practices
Deploy the blueprint
AI and machine learning
Content overview
Agentic AI
Overview
Choose agentic architecture components
Choose an agent design pattern
Multi-agent AI system
Multi-agent private networking patterns
Multi-tenant agentic AI system
Single-agent AI system using ADK and Cloud Run
Use cases
Administer interactive learning
Automate data science workflows
Build a cross-cloud open data lakehouse
Build a trusted agentic system with Google Maps Platform
Classify multimodal data
Implement agentic analytics for distributed data
Enable live bidirectional multimodal streaming
Multimodal GraphRAG resource orchestration
Orchestrate access to disparate systems
Orchestrate security operations workflows
Generative AI
Overview
Generative AI with RAG
Overview
Private connectivity for RAG-capable generative AI applications
RAG infrastructure using Gemini Enterprise and Agent Platform
RAG infrastructure using Agent Platform and Vector Search
RAG infrastructure using Agent Platform and AlloyDB
RAG infrastructure using GKE and Cloud SQL
GraphRAG infrastructure using Agent Platform and Spanner Graph
Harness CI/CD pipeline for RAG applications
Deploy an enterprise generative AI and ML model
Deploy and operate generative AI applications
Networking for AI inference model serving on all backends
Networking for AI inference model serving on GKE
Use cases
Automate utilization-review of health insurance claims
Generate personalized marketing campaigns
Generate personalized product recommendations
Generate podcasts from audio
Generate solutions for customer support questions
ML applications and operations
Overview
Best practices for implementing ML on Google Cloud
Guidelines for high-quality, predictive ML solutions
MLOps using TensorFlow Extended, Agent Platform Pipelines, and Cloud Build
MLOps: Continuous delivery and automation pipelines in machine learning
Build an ML vision analytics solution with Dataflow and Cloud Vision API
Reference architecture
