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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 …
The Imperative for Machine Learning Compilation From Development to Deployment: The Core Challenge Machine Learning Compilation (MLC) represents the critical technological bridge that transforms a machine learning model from its Read More …
Section 1: The Challenge of Scale and the Parallelism Paradigms 1.1 The Memory and Compute Wall in Modern Deep Learning The field of deep learning, particularly in natural language processing Read More …
The Reality of Production Models: Bridging the Offline-Online Gap The lifecycle of a machine learning model does not conclude upon achieving a high accuracy score on a validation dataset. Instead, Read More …
Executive Summary This report provides a comprehensive analysis of multi-model serving (MMS), a critical paradigm for efficiently deploying large numbers of machine learning models on shared infrastructure. We deconstruct the Read More …
Executive Summary The deployment of machine learning models has reached a critical inflection point, moving from a paradigm dominated by perpetually running, provisioned infrastructure to one that embraces the efficiency Read More …