教程中心

AI SLA and SLO Management Overview Defining and meeting SLAs/SLOs for AI services Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI() class Handler: """

Build production payment fraud prevention systems using graph neural networks, transaction sequence models, real-time feature engineering, and explainable AI for compliance and dispute resolution.

Explore AI applications in healthcare documentation including ambient clinical intelligence for automatic note generation, SNOMED/ICD-10 coding, clinical NER, and AI-assisted clinical decision support.

详解AI系统在GDPR、CCPA、中国个保法和EU AI Act下的合规要求，包括合法性基础、数据最小化、算法透明度、被遗忘权实现和隐私影响评估，提供可操作的合规检查清单。

AI 3D Generation: 2025 Guide Overview AI-powered 3D model and scene generation tools Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI() class Handler:

探讨AI在诉讼分析和法律研究中的应用，包括案例相关性搜索、判决结果预测、法官偏好分析和反对律所策略研究，以及Westlaw Edge、LexisNexis AI的功能评测。

介绍如何利用AI技术构建企业合规监控系统，包括监管文件自动采集、法规变更影响评估、合规差距分析和监管报告自动化，以及在金融、医疗、能源行业的应用。

Feature Store Implementation Overview Building and managing ML feature stores for production. This guide covers practical implementation for production ML systems. Why This Matters in MLOps Modern ML systems require rigorous operations practices:

Implement advanced AI analytics for supply chain including demand sensing with external signals, supplier risk prediction, disruption detection, and autonomous procurement decisions.

Quantization Explained: Technical Deep Dive Overview INT4/INT8 quantization for faster, smaller model inference. This comprehensive guide covers everything you need to know for production implementation. Why It Matters Quantization Explained: Tec

Learn practical reinforcement learning applications beyond games including supply chain optimization, cloud resource management, recommendation systems, and robotics control with modern RL libraries.

PEFT: Parameter-Efficient Methods Overview Overview of all parameter-efficient fine-tuning approaches. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets p

Build and deploy AI resume screening systems that are effective, fair, and legally compliant, covering bias detection, disparate impact analysis, explainability, and EEOC compliance.

介绍AI在智慧城市建设中的核心应用，包括智能交通信号控制、公共安全监控、城市基础设施预测维护和市政服务AI化，以及城市AI引发的隐私和公民自由问题。

全面介绍仓储机器人的技术演进和AI核心能力，包括SLAM导航、路径规划、抓取感知、多机协调和人机协作，以及自动化仓储的ROI分析和未来趋势。

介绍AI在能源管理中的应用，包括太阳能/风能发电预测、电网负荷预测、储能优化调度和需求响应自动化，以及AI对实现碳中和目标的贡献。

Build systematic incident response processes for AI systems including runbooks for common failure modes, root cause analysis frameworks, rollback procedures, and post-incident learning.

Comprehensive guide to practical techniques for reducing LLM hallucinations in production systems, including RAG, retrieval verification, self-consistency sampling, and chain-of-verification prompting.

详细介绍AI预测性维护系统的技术架构，从边缘计算数据采集、时序特征提取、故障预测模型到维护工作流自动化，以及在制造业的实际部署经验。

Learn AI applications for geospatial analysis including satellite image classification, change detection, flood mapping, urban growth analysis, and building location intelligence with deep learning.

Explore when and how deep learning approaches (TabNet, FT-Transformer, SAINT) outperform gradient boosting on tabular data, with practical implementation and hyperparameter guidance.

Build an intelligent search system combining Elasticsearch with AI for semantic understanding, neural reranking, personalized autocomplete, and query expansion for superior search relevance.

Practical guide to federated learning using the Flower framework, covering federation strategies, differential privacy, communication efficiency, and real-world deployment for healthcare and fintech.

LoRA Mathematics: Technical Deep Dive Overview The mathematical foundation of Low-Rank Adaptation. This comprehensive guide covers everything you need to know for production implementation. Why It Matters LoRA Mathematics: Technical Deep Dive is

How to Fine-tune Llama 3 on Custom Data 2026 Introduction In this tutorial, you'll learn how to **Fine-tune Llama 3 on Custom Data**. By the end, you'll have a working **specialized AI model** that you can deploy and extend. **Prerequisites:** - E

Build production Named Entity Recognition systems for custom entity types using spaCy and transformer models, covering annotation strategies, active learning, and deployment optimization.

Build production computer vision systems using YOLO v11 for object detection, including custom training, model optimization with TensorRT, edge deployment, and real-time video stream processing.

Comprehensive guide to advanced time series forecasting using neural architectures including N-BEATS, PatchTST, Chronos, and TimeGPT, with practical implementation and model selection guidance.

Learn knowledge distillation techniques to create small, fast student models that mimic large teacher model performance, covering task distillation, feature-level distillation, and production deployment.

Build a production document Q&A system from PDF parsing and chunking through vector indexing, RAG-based answering, citation extraction, and enterprise deployment with access controls.

介绍如何利用NLP和LLM技术分析金融文本数据，包括SEC文件解析、财报电话会议情绪分析、新闻事件驱动策略和另类数据整合，构建基于文本的量化交易因子。

Build production anomaly detection systems for time series data using statistical methods, isolation forest, LSTM autoencoders, and modern time series foundation models for infrastructure and IoT monitoring.

深入介绍智能投顾系统的技术架构，包括风险偏好评估、现代投资组合理论（MPT）AI增强、税收损失收割自动化和客户沟通个性化，以及各国智能投顾监管现状。

Step-by-step tutorial to build a recommendation engine from simple collaborative filtering through neural two-tower models, covering implicit feedback handling, cold start, and A/B testing.

深入介绍图神经网络（GNN）在支付反欺诈中的应用，包括交易关系图构建、欺诈模式识别、实时推理优化和与规则引擎的结合策略。

介绍如何构建AI信用评分模型，包括特征工程、模型选择、公平性评估和监管合规，以及主要金融机构的AI风控实践经验。

Comprehensive guide to LLM application architecture patterns from simple prompt-response to complex multi-agent systems, with a decision framework for choosing the right architecture.

详解基于机器学习的量化交易系统开发全流程，包括特征工程、模型选择、回测框架、风险管理和实盘部署，以及常见的过拟合陷阱和规避方法。

Learn rigorous A/B testing methodology for AI features including power analysis, sample size calculation, sequential testing, Bayesian approaches, and avoiding pitfalls like peeking and p-hacking.

Build a scalable LLM microservice using FastAPI with async endpoints, Redis caching, rate limiting, health checks, and Docker containerization for production deployment.

Implement robust privacy protection for AI applications handling sensitive user data, covering PII detection and redaction, differential privacy, federated learning, and GDPR compliance requirements.

Design production-grade AI content moderation systems for text, images, and video, covering classification models, human review workflows, policy management, and appeals processes.

介绍AI自动评测技术的实现原理，包括作文评分、代码评测、项目评估和多语言评分，以及如何确保AI评分的公平性和可解释性。

Build comprehensive observability for production LLM applications using Langfuse, Helicone, and Prometheus, covering trace collection, metric dashboards, alerting, and cost monitoring.

Planning and Execution Agent Overview Agent with high-level planning and step-by-step execution. This guide covers architecture, implementation, and production deployment of AI agents. Agent Architecture ``` User Input ↓ Agent Orchestrator

Design patterns and best practices for building robust AI-powered REST and WebSocket APIs including streaming responses, idempotency, rate limiting, versioning, and managing non-deterministic outputs.

RLHF Explained Simply: Technical Deep Dive Overview Reinforcement Learning from Human Feedback demystified. This comprehensive guide covers everything you need to know for production implementation. Why It Matters RLHF Explained Simply: Technical

Master AI function calling and tool use patterns for building reliable agents, covering tool design, error handling, parallel tool execution, and preventing tool abuse.