AI 开发 教程
系统化的 AI 开发学习路径。从提示词工程到模型微调，从 RAG 到 Agent 开发，覆盖 AI 应用开发全栈知识。共 142 篇教程，18 个分类，入门到高级全覆盖。
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DeepSeek Deployment and Operations Tutorial
Complete DeepSeek model deployment process: vLLM deployment configuration, load balancing, auto-scaling, monitoring and alerting, and cost optimization. Includes Docker and Kubernetes deployment solutions and cloud platform deployment guides, covering all scenarios from single machine to cluster.
阅读教程 →AI Application Observability: Logging, Monitoring, and Alerting in Practice
Production AI applications require a comprehensive observability system. This article details token usage monitoring, API latency tracking, error rate alerts, cost analysis dashboards, and logging best practices to help you build reliable AI services.
阅读教程 →Complete Guide to Local Deployment of DeepSeek Models
Cloud APIs are great, but many scenarios require local deployment—data security, low latency, and offline use. This article comprehensively explains local deployment solutions for DeepSeek models, covering mainstream tools such as Ollama, vLLM, and llama.cpp, from hardware selection to performance tuning.
阅读教程 →AI API Cost Optimization in Practice
The token cost of AI APIs may seem cheap, but costs can spiral out of control at scale. This article systematically explains practical strategies for optimizing AI API costs, covering prompt compression, semantic caching, model routing, and batch processing, helping you reduce your monthly bill by 50%-80%.
阅读教程 →Best Practices for Containerizing AI Services
Containerizing AI model services is the first step in production deployment. This article explains a complete solution for Docker image optimization, GPU support, health checks, and Kubernetes deployment.
阅读教程 →AI Testing in CI/CD Pipelines
Integrating AI model testing into CI/CD pipelines is key to continuously delivering high-quality AI services. This article explains a complete solution for automated evaluation, regression testing, performance benchmarks, and deployment gates.
阅读教程 →Panorama of Large Model Inference Acceleration Technologies
Inference speed directly determines user experience. This article systematically explains inference acceleration techniques such as KV Cache, Speculative Decoding, model quantization, and operator fusion, helping you comprehensively master large model inference optimization from principles to practice.
阅读教程 →High-Performance Inference Deployment with vLLM
vLLM is currently the most advanced open-source LLM inference engine. This article provides an in-depth explanation of vLLM's PagedAttention principle, production environment configuration, performance tuning strategies, and monitoring solutions to help you build high-throughput inference services.
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