Description
As enterprises mature in their AI journey, they frequently encounter scenarios where single-model solutions fall short of addressing complex business requirements. Multi-model architectures – deployments where multiple AI models work in concert to solve sophisticated problems – have emerged as a powerful solution. However, these architectures introduce unique challenges in orchestration, resource management, and performance optimization.
Managing multi-model architectures in production requires a delicate balance between system complexity and operational efficiency. Organizations must navigate challenges ranging from model dependency management and resource allocation to ensuring consistent performance across the entire model ecosystem. Here is a framework for successfully implementing and managing multi-model architectures at scale.
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