A Comprehensive Analysis of Modern LLM Inference Optimization Techniques: From Model Compression to System-Level Acceleration

The Anatomy of LLM Inference and Its Intrinsic Bottlenecks The deployment of Large Language Models (LLM) in production environments has shifted the focus of the machine learning community from training-centric Read More …

The Post-LLMOps Era: From Static Fine-Tuning to Dynamic, Self-Healing AI Systems

Executive Summary The rapid proliferation of Large Language Models (LLMs) has catalyzed the emergence of a specialized operational discipline: Large Language Model Operations (LLMOps). While essential for managing the current Read More …

Kubeflow: Streamlining Machine Learning Workflows on Kubernetes

Introduction In the ever-evolving landscape of machine learning and artificial intelligence, managing the end-to-end lifecycle of models can be a challenging endeavour. From data pre-processing and model training to deployment Read More …