AI Service Optimization Stack Design Guide: Building Cloud Architecture That Delivers Both Cost Efficiency and Reliability
This guide presents a practical cloud architecture approach for handling rapidly growing AI workloads. Combining performance and cost-efficiency analysis, it provides a Terraform-based stack and monitoring strategy you can apply immediately
The Practical Barriers to LLM Services: Cost and Speed, and How Model Compression Solves Them
The massive operating costs and slow inference speeds that come with the explosive growth of LLMs are the biggest obstacles to production adoption. This post defines why model compression is necessary and clearly outlines three core optimiz
From Jupyter Notebook to Production: The Complete Guide to LLM Deployment—Taming Speed and Cost
A practical guide to fixing the LLM slowdowns and cost blowups you hit after PoC. It covers everything engineers need, from modern optimizations like vLLM and Paged Attention to real-world deployment architecture.
Reducing GPU Waste on Kubernetes: Device Plugin, MIG Partitioning, and Workload Placement
Are expensive GPUs sitting idle and burning budget? This guide lays out a complete roadmap for running AI workloads at peak efficiency on Kubernetes—from GPU virtualization and NVIDIA MIG to MLOps-oriented scaling strategies.
Breaking Data Silos in the LLM Era: Architecting Toward Data Mesh
The explosive growth of LLM and RAG technologies is exposing structural data bottlenecks. This post diagnoses the limits of traditional data lakes and presents the four principles of Data Mesh architecture—treating data as a product—along w
Escaping Data Silos: A Roadmap for Building a Unified Data Lake to Maximize Business Value in the AI Era
If scattered data is causing you to miss business opportunities, data silos are the problem. This guide diagnoses the root causes of data silos for data architects and decision-makers, and presents a phased blueprint for building a data lak