Jupyter Models Slow on Server Deployment? A Complete MLOps Optimization Roadmap
This post systematically solves the challenges of deploying trained AI models into real service environments. From model profiling and quantization to TensorRT optimization and MLOps pipeline construction, it delivers a practical roadmap fo
Three Ways to Speed Up LLM Inference Compared: Quantization, Pruning, and KV Cache Optimization
A deep dive into three core techniques—Quantization, Pruning, and KV Cache optimization—that solve the inference speed and cost problems of deploying LLMs in production. We cover the principles, trade-offs, and practical optimization strate
Edge AI Inference Engines Compared: TFLite vs ONNX Runtime and How to Choose for Your Project
A practical guide to choosing the inference engine — the most important decision when deploying AI models to edge devices. We compare the strengths, weaknesses, and performance of TFLite, ONNX Runtime, and related engines, and give clear, s
Advanced MLOps Guide: Strategies for Building CI/CD/CM Workflows to Elevate Lab Models to Production Grade
Reliably deploying an AI model that succeeded in a notebook into a real production environment is the biggest challenge. This guide presents a complete blueprint for building an MLOps CI/CD/CM workflow, covering automated A/B testing, drift
Beyond Prototypes to Production: A DevOps Guide to Reliable Deployment and Cost Optimization for LLM Agents
A practical guide to taking LLM agents from research into real business services. From building CI/CD pipelines to token cost tracking and fallback patterns, this is a complete production-grade LLMOps playbook.
[LLMOps Guide 1/N] Architectural Design for Protecting LLM Agent/RAG Systems in Production
Taking an LLM-based service beyond PoC and into real production is a new kind of challenge. This guide presents the core principles of an LLMOps architecture—and practical monitoring strategies—for systematically detecting and defending aga
Beyond the Cloud to Smartphones: A Complete Guide to Edge AI for On-Device LLM Implementation
Tired of API call costs and latency? This article provides a complete, step-by-step guide to the full architecture and model lightweighting techniques for implementing Edge AI and on-device LLMs, so you can master practical deployment metho
[Part 2] Make Your Edge AI Models This Lightweight: A Practical Optimization Guide from Quantization to Deployment
Hitting performance bottlenecks when moving AI models from the cloud to edge devices? This guide covers model compression (quantization and pruning) with TFLite, ONNX, and more—from theory through hands-on optimization and deployment on Jet
Beyond the Cloud to the Edge: An Essential Tool Comparison Guide for Embedded AI (TFLite vs. ONNX Runtime)
Does deploying AI models onto real hardware (Raspberry Pi, Jetson, and similar devices) feel harder than training them? This guide compares TensorFlow Lite and ONNX Runtime—the core runtimes for edge computing—and walks through deployment w
Implementing On-Device AI with WebAssembly (Wasm): A Practical TFLite vs ONNX Deployment Guide
Catch up with the latest trend of running AI models in the browser with no server dependency. Centered on WebAssembly (Wasm), we compare the pros and cons of TFLite and ONNX and present a practical 3-step roadmap from model compression to i
Building MLOps Beyond Jupyter: CI/CD, Model Registry, and Drift Detection
Struggling to take AI models from the lab to production? This guide covers MLOps fundamentals, fully automated pipelines with Kubeflow, and production drift detection—a practical architecture roadmap every senior engineer should know.
Practical Guide: Scenario-Based Edge Computing Architecture Design from Smart Factories to Autonomous Driving
Going beyond conceptual overviews, this post shows how to design edge computing architectures optimized for real industrial scenarios such as smart factories and autonomous driving. It offers an in-depth architect-level comparison of techno