Reliability
The reliability best practices introduced in this paper are represented by at least of one of the following principles:
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Design for distributed resilience: Deploy your generative AI workloads across multiple regions and availability zones to avoid single points of failure. By distributing model endpoints, embedding data, and agent capabilities geographically, you create a system that remains operational even if individual components or entire regions become unavailable. This approach helps you achieve consistent service delivery and helps maintain performance during regional disruptions or network issues.
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Implement robust error management: Monitor generative AI workflows for robustness and completion, and implement automated recovery mechanisms when errors occur. Prevent cascading failures for agent workflows and verify that your system recovers predictably. This allows you to maintain service continuity even when individual components, such as model inference calls or embedding operations, experience issues.
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Standardize resource management through catalogs: Maintain centralized catalogs for prompts and models to maintain consistent, governed access to resources across your generative AI workload. By implementing standardized catalogs, you create a single source of truth for critical components, enable version control, and facilitate updates or rollbacks when needed. This reduces the risk of using outdated or inappropriate resources while simplifying management and governance.
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Architect for intelligent scalability: Design your generative AI systems to automatically adjust resources based on actual utilization patterns and demand. By implementing dynamic scaling and load balancing across your infrastructure, you can maintain optimal performance while avoiding resource saturation. This approach helps you achieve efficient resource usage while maintaining consistent performance under varying loads, without over-provisioning or under-provisioning.
Focus areas
