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The Data-Gated Lifecycle: Why 90% of AI Prototypes Fail The contemporary boom in Artificial Intelligence (AI) is predicated on the dual pillars of algorithmic innovation and data availability. Yet, while Read More …
Executive Summary The final, critical step in the Machine Learning (ML) lifecycle—deploying a model into production—represents the bridge between a trained artifact and tangible business value.1 However, this step is Read More …
Section 1: Redefining the Customization Stack: The Relationship Between Domain Adaptation, Fine-Tuning, and Customization 1.1 Deconstructing the Terminology: Domain Adaptation as the Goal, Fine-Tuning as the Mechanism The landscape of Read More …
Executive Summary: The Great Unbundling of AI Inference The monolithic, GPU-dominated era of artificial intelligence is fracturing. The “LLM Inference Wars” are not a single battle but a multi-front conflict, Read More …