最新博客文章
10 / 200AI 开发实战教程，从零到一构建你的 AI 应用。
All200
Beginner32
Intermediate92
Advanced76
All
Prompt Engineering12
RAG Retrieval Augmented17
Agent Development34
Model Fine-tuning21
Deployment and Operations25
AI Application Development40
DeepSeek API16
Multimodal Development6
AI Safety6
Model Inference3
Retrieval Systems1
RAG Retrieval1
Model Architecture3
API Development1
Agent Development10
Model Deployment1
Multimodal1
Model Evaluation2
Tutorial
Date
High-Performance Inference Deployment with vLLM
Deployment and Operations
Intermediate
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.
AI Application Observability: Logging, Monitoring, and Alerting in Practice
Deployment and Operations
Intermediate
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.
DeepSeek Deployment and Operations Tutorial
Deployment and Operations
Intermediate
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.
AI Testing in CI/CD Pipelines
Deployment and Operations
Intermediate
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.
Panorama of Large Model Inference Acceleration Technologies
Deployment and Operations
Intermediate
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.
AI Application Cost Engineering: Token Ledgers, Unit Economics, and Cost Governance
Deployment and Operations
Intermediate
Turn a month-end invoice into a daily operational metric. This guide covers a cost event schema and ledgers by feature, tenant and model, cost per session and effective tokens, a unit economics model built from contribution margin, what prompt caching, quantization, small-model routing and batching actually save once corrected for success rate, and nine cost attribution traps.
Canary Releases for AI Apps: Model, Prompt, and Retrieval Changes Together
Deployment and Operations
Intermediate
When one release changes the model, the prompt, and the retrieval layer at once, attribution gets harder than rollback. This article freezes any request into a content-addressed triple, separates what shadow traffic and A/B splits can and cannot answer, and outlines regression gates covering structure, semantics, retrieval, cost, and latency.
Best Practices for Containerizing AI Services
Deployment and Operations
Intermediate
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.
AI API Cost Optimization in Practice
Deployment and Operations
Intermediate
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%.
Complete Guide to Local Deployment of DeepSeek Models
Deployment and Operations
Intermediate
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.
