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AI Agent 从入门到实战：概念理解、MCP 使用、平台实操、工作流自动化

1252

教程总数

234

入门教程

42

实操教程

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RAG 检索增强生成AI Agent 与多智能体模型部署与生产化工作流与自动化OpenAI 开发实战Claude / Anthropic 开发LangChain / LangGraph模型微调与训练Prompt 工程MCP（Model Context Protocol）评估、测试与可观测AI 安全与合规API 与集成开发
进阶其他

LangSmith for LLM Evaluation: Building Systematic Feedback Loops

Trace collection, evaluation datasets, A/B testing, and regression detection

LangSmith LLM 评估工作流（2026）：追踪→数据集→评估器(含 LLM-as-judge)→实验四件套，把"感觉变好了"变成可测进步。含 @traceable 代码、每周评估闭环、LLM 裁判的偏差校准，及 vs Langfuse。

LangSmithevaluation
10分钟
进阶其他

AI Data Pipelines: ETL and Preprocessing for ML Models

Build robust data pipelines that feed high-quality data to AI models

Design and implement production-grade data pipelines for ML training and inference. Covers data validation, feature engineering, handling missing data, and pipeline orchestration with Prefect and Airflow.

data-pipelineetl
38分钟
进阶其他

AI Observability: Monitoring LLMs and ML Models in Production in 2025

Track quality, cost, drift, and failures for AI systems with LLMOps observability platforms

Deploying AI without observability is flying blind. This guide covers LLM-specific monitoring with LangSmith, Arize Phoenix, and Weights & Biases, detecting hallucinations and quality degradation, monitoring embedding drift for RAG systems, tracking token costs and latency SLAs, setting up alerting for AI failures, and building dashboards that give engineering and product teams visibility into AI system health.

LLM ObservabilityAI Monitoring
20分钟