AI Model Lifecycle Management in Commercial Hardware

Abstract

The rapid spread of AI across the commercial sector has bred the need for proper arsenal to manage AI models across the span of their lifecycle, especially given the deployment on hardware with various constraints. This paper looks at the pivotal processes concerning the life and death of an AI model, which is designing, training, deployment, monitoring, and continuous improvement, within the realm of commercial hardware systems. It pays special attention to the tension between hardware capabilities and model-level performance due to insufficient compute resources, power efficiency requirements, and real-time processing demands. In this vein, the study considers current tools, frameworks, and methodologies that aid lifecycle automation, model optimization, and standards compliance-including security and regulatory requirements. It also investigates futuristic trends such as federated learning, edge computing, and MLOps for advancing lifecycle workflows. Via thorough theoretical analysis coupled with experimental verification, it mounts an argument for best practice and a systematic approach to scalable, dependable, and secure management of AI models on commercial hardware. The findings put in place a strong argument for an integrated lifecycle strategy capable of keeping models performant, resilient, and ethically deployed in an increasingly AI-driven world.