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Executive Summary The artificial intelligence hardware market is undergoing a strategic fragmentation, moving from the historical dominance of the general-purpose Graphics Processing Unit (GPU) to a new triad of specialized Read More …
Section 1: Executive Summary The Zero Redundancy Optimizer (ZeRO) represents a paradigm-shifting technology from Microsoft Research, engineered to dismantle the memory bottlenecks that have historically constrained large-scale distributed training of Read More …
The Imperative for Model Compression in Modern Deep Learning The discipline of model compression has transitioned from a niche optimization concern to a critical enabler for the practical deployment of Read More …
Section 1: The Imperative of Model Dynamism in Production Environments The deployment of a machine learning (ML) model into a production environment marks not an end but a beginning. Unlike Read More …
The Strategic Imperative: Why Model Registries are the Cornerstone of Modern MLOps In the landscape of Machine Learning Operations (MLOps), the model registry has emerged as a foundational component, evolving Read More …
Executive Summary The proliferation of Large Language Models (LLMs) has been constrained by their immense computational and memory requirements, making efficient inference a critical area of research and development. Post-Training Read More …
Section 1: The Imperative for Systematic Tracking in Modern Machine Learning 1.1 Beyond Ad-Hoc Experimentation: Defining the Discipline of Experiment Tracking The development of robust machine learning models is an Read More …
Section 1: The Imperative for Full-Stack Reproducibility in Machine Learning The successful deployment and maintenance of machine learning (ML) models in production environments demand a level of rigor that extends Read More …
Introduction: The Modern Imperative for Optimized AI Inference The rapid evolution of artificial intelligence has created a significant divide between the environments used for model training and those required for Read More …
Executive Summary The exponential growth in the size of deep learning models has precipitated a significant challenge in high-performance computing: the “memory wall.” Traditional distributed training methods, particularly Distributed Data Read More …