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。
找到 18 篇教程
DeepSeek Inference Acceleration In-Depth Tutorial
Comprehensively master DeepSeek model inference acceleration techniques. In-depth comparison of four major engines: vLLM, SGLang, TensorRT-LLM, and llama.cpp, from deployment to tuning, from single GPU to distributed, providing a complete inference acceleration solution.
阅读教程 →DeepSeek Model Selection and Deployment Guide
Detailed explanation of the DeepSeek full model series: capability characteristics, applicable scenarios, and deployment solutions for models such as V3, R1, Coder, and VL2. A complete guide from model selection to production deployment, including API calls and local deployment tutorials.
阅读教程 →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 →DeepSeek Performance Tuning Tutorial
Complete DeepSeek performance tuning guide: GPU memory optimization, batching strategies, quantization techniques, concurrency control, and latency optimization. Includes profiling tool usage, performance benchmarking, and best practices to achieve an optimal balance between throughput and latency.
阅读教程 →DeepSeek Model Download and Installation
Complete Guide to DeepSeek Model Download and Installation: Hugging Face Download, Ollama One-Click Deployment, Quantization Version Selection, and VRAM Requirements Assessment. Includes download links, installation steps, and verification methods for all DeepSeek models.
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 →LLM Inference Performance Optimization: vLLM and Continuous Batching
After model deployment, throughput and latency are core metrics. This article provides an in-depth analysis of vLLM's PagedAttention and Continuous Batching principles, and gives a complete optimization roadmap from benchmark testing, parameter tuning to production deployment.
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 →Monitoring and Observability for AI Applications
Observability for AI applications is far more complex than for traditional applications—it requires monitoring multiple dimensions such as LLM call latency, token consumption, hallucination rate, and user satisfaction. This article builds a complete AI application monitoring system, covering metric collection, alert configuration, cost tracking, and quality monitoring.
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 →GPU Cluster Management and Scheduling
Managing a cluster of dozens of GPUs requires professional scheduling strategies. This article explains practical experience in GPU resource management, task priority scheduling, memory fragmentation cleanup, and multi-tenant isolation.
阅读教程 →In-Depth Guide to GPU-Accelerated Inference Optimization
GPU inference optimization is key to reducing the cost of large model deployment. This article provides an in-depth explanation of core technologies such as FlashAttention, quantized inference, TensorRT acceleration, and continuous batching to help you maximize GPU utilization.
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 →Best Practices for Large Model API Service
API-fying LLM capabilities is a key step for enterprise-level applications. This article systematically explains best practices for LLM API services from dimensions such as architecture design, rate limiting strategies, cache optimization, and security protection.
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.
阅读教程 →Comparison of Local Deployment Options for Large Models
Local deployment of large models is a key solution for ensuring data security and controlling costs. This article compares mainstream deployment solutions such as Ollama, vLLM, and llama.cpp, helping you make a choice from dimensions such as ease of use, performance, and hardware requirements.
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