From IDP to AIDP: Evolving your platform for the machine learning age - Max Körbächer

Evolving an Internal Developer Platform (IDP) into an AI-enabled platform (AIDP) requires shifting focus from simple stateless applications to supporting complex machine learning lifecycles. This transformation prioritizes treating AI models as first-class citizens, necessitating robust infrastructure for GPU resource sharing, large-scale data management, and efficient scheduling. By integrating tools like KServe and leveraging Kubernetes for orchestration, organizations can bridge the gap between developers, data engineers, and researchers. A successful AIDP strategy emphasizes self-service capabilities, cost visibility, and standardized environment templates to democratize AI development. Beyond technical efficiency, this platform-as-a-product approach provides the necessary guardrails for regulatory compliance, such as ISO 42001, ensuring organizations maintain control over AI usage, data integrity, and performance monitoring while navigating the rapidly evolving AI ecosystem.