Uplatz is a global IT Training & Consulting company
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 …
The Paradigm of Data Parallelism in Deep Learning Data parallelism is a foundational strategy in Data-Parallel computing that has become the most prevalent method for accelerating the training of deep Read More …
The Imperative for Intelligent GPU Orchestration Beyond Raw Power: Defining GPU Orchestration as a Strategic Enabler In the contemporary landscape of artificial intelligence (AI) and high-performance computing (HPC), Graphics Processing Read More …
Executive Summary Gradient accumulation is a pivotal technique in modern deep learning, designed to enable the training of models with large effective batch sizes on hardware constrained by limited memory.1 Read More …
Part I: The Imperative of Monitoring in the MLOps Lifecycle The operationalization of machine learning (ML) models into production environments marks a critical transition from theoretical potential to tangible business Read More …
Part I: The Paradigm of Edge Machine Learning The deployment of artificial intelligence has historically been synonymous with vast, powerful cloud data centers. This centralized model, where data is collected Read More …
Executive Summary The proliferation of massive datasets has necessitated a paradigm shift in data architecture, moving away from monolithic systems toward flexible, scalable, and reliable distributed platforms. This report provides Read More …
I. Foundational Principles of Model Parallelism 1.1. The Imperative for Scaling: The Memory Wall The field of deep learning is characterized by a relentless pursuit of scale. State-of-the-art models, particularly Read More …