SupplyLogic

As enterprises accelerate their adoption of intelligent systems, the need for flexible, scalable architectures has never been more critical. Organizations are moving away from monolithic AI deployments toward composable frameworks that allow teams to swap components, test new models, and integrate specialized capabilities without tearing down entire systems. This shift is reshaping how Machine Learning Model Development teams collaborate with Data Engineering and Architecture groups, enabling faster iteration cycles and more resilient AI Infrastructure Management. Whether you're optimizing inference latency for edge deployments or orchestrating multi-agent workflows across distributed environments, the ecosystem of tools, frameworks, and resources supporting this movement has matured significantly over the past year. This resource roundup brings together the most actionable tools, frameworks, community hubs, and technical reads that practitioners are using to implement Modular AI I...