Most organizations struggle to move AI projects from pilot to production due to inadequate data infrastructure and operating models.
Data mesh enables scalable decentralization through four key principles: domain ownership, peer-to-peer data sharing, federated governance, and self-service management.
AI mesh builds on data mesh by introducing domain-specific agents, communication standards, and treating intelligence as a composable product.
Progressing from a basic data mesh to autonomous AI systems requires ensuring data is findable, understandable, trustworthy, and consumable for agents.
Using ESG reporting as an example illustrates how data mesh solves cross-domain coordination challenges by establishing clear data ownership and scalability.
A practical data mesh implementation on AWS uses services like Amazon SageMaker and DataZone to manage producers, consumers, and a central data catalog.
An AI mesh architecture on AWS combines a data mesh foundation with an intelligence layer using distributed agents, a registry, and Amazon Bedrock Agent Core.
The demo showcases querying domain-specific agents and an orchestrator agent to automatically gather and consolidate cross-domain data for an ESG report.
AI governance builds upon existing data mesh frameworks by adding agent-specific layers for guardrails, model drift monitoring, and regulatory compliance.
A practical adoption strategy involves starting with a few mature domains, building the platform foundation, deploying agents with guardrails, and then scaling incrementally.
Jobs that call for the skills explored in this talk.