教程中心

深入介绍LLM在自动驾驶中的最新应用，包括端到端驾驶模型、语言指令理解、复杂场景常识推理和仿真数据生成，以及当前自动驾驶AI的技术成熟度和商业化进展。

Self-Healing Agent Overview Agent that detects and recovers from its own errors. This guide covers architecture, implementation, and production deployment of AI agents. Agent Architecture ``` User Input ↓ Agent Orchestrator ↓ ┌───────────

Deploy Llama 3.1 8B on AWS Graviton3 Overview Run Llama 3.1 8B directly on AWS Graviton3 for ARM cloud inference. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: ARM Neoverse · 32-256GB Installation ```bash Ins

介绍神经形态计算（Neuromorphic Computing）的原理和最新进展，包括Intel Loihi 2、IBM True North等芯片的设计创新，以及与传统GPU相比的能效优势和当前局限性。

客观评估量子计算与AI融合（量子机器学习）的真实进展，包括量子神经网络、量子优化和量子模拟的现状，以及未来10-20年量子AI的实际应用前景。

Multi-Modal Agent Systems: Advanced Guide Overview Agents that process and generate across modalities. This comprehensive guide covers everything you need to know for production implementation. Why It Matters Multi-Modal Agent Systems: Advanced G

深入介绍小型语言模型的技术特点和部署方案，包括Phi-3、Gemma 2、Llama 3.2等模型的对比测评，以及在iOS/Android、Raspberry Pi和工业边缘设备上的部署实践。

Fine-tuning Evaluation Overview Evaluating fine-tuned models with domain benchmarks. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelerate

Tool-Calling Agent with OpenAI Overview Building function-calling agents with OpenAI tools API. This guide covers architecture, implementation, and production deployment of AI agents. Agent Architecture ``` User Input ↓ Agent Orchestrator

AI 金丝雀分析：安全的模型上线（2026）：把新版本灰度给小流量、按运营+质量+安全指标对比阈值自动晋级或回滚。含机制、Argo Rollouts/Flagger、按区域灰度、配合回退链，给模糊的"更好"装上自动闸门。

Social Media Agent Overview Automated social media content creation and posting agent. This guide covers architecture, implementation, and production deployment of AI agents. Agent Architecture ``` User Input ↓ Agent Orchestrator ↓ ┌─────

Real-time Multimodal AI: 2025 Guide Overview Building with GPT-4o Realtime and Gemini Live API Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI() class Han

Kubernetes for AI Workloads Overview Deploying and scaling AI services on Kubernetes. This guide provides practical, production-ready implementations. **Category**: ai-infrastructure **Primary Tool**: kubernetes **Tags**: infrastructure, devop

Building Data Analysis Agent with AI Agents 2026 Introduction AI agents that can analyze datasets and generate insights autonomously are transforming how developers work. This guide shows you how to build a production-ready Data Analysis Agent usin

Airflow for ML Orchestration Overview Using Apache Airflow to schedule and monitor ML pipelines. This guide covers practical implementation for production ML systems. Why This Matters in MLOps Modern ML systems require rigorous operations practic

Recursive AI Systems: Advanced Guide Overview AI systems that improve themselves iteratively. This comprehensive guide covers everything you need to know for production implementation. Why It Matters Recursive AI Systems: Advanced Guide is increa

Explore AI applications in clinical trials including patient-trial matching, protocol design optimization, dropout prediction, adverse event detection, and AI-accelerated regulatory submissions.

Build AI-powered social media analytics systems for brand monitoring, trend detection, sentiment tracking, influencer identification, and crisis early warning using NLP and ML techniques.

Master explainable AI techniques including SHAP values, LIME, integrated gradients, and attention visualization to interpret machine learning models for debugging, compliance, and stakeholder communication.

Comprehensive security guide for LLM applications covering prompt injection defense, jailbreak resistance, output filtering, and building secure AI systems that resist adversarial manipulation.

Design and implement adaptive learning systems using knowledge tracing models, spaced repetition algorithms, and LLM-powered tutoring for personalized educational experiences at scale.

Learn AI applications for stock market analysis including news sentiment analysis, technical pattern recognition, earnings call analysis, and ML-based portfolio optimization with proper risk management.

介绍具身智能（Embodied AI）的最新研究进展，包括RT-2/RT-X视觉语言动作模型、语言指令跟随机器人、灵巧手操作学习和人形机器人从实验室到工厂的技术路径。

探讨世界模型（World Model）的概念和前沿进展，包括Yann LeCun的JEPA架构、视频生成作为隐式世界模型、游戏AI中的世界模型和物理模拟，以及世界模型对AGI研究的重要性。

Implement robust ML model lifecycle management using MLflow Model Registry, covering model versioning, staging environments, approval workflows, and automated deployment pipelines.

Time-Aware RAG: Complete Implementation 2026 Overview Time-Aware RAG is a specialized retrieval pattern that focuses on weighting recent documents higher in retrieval. This guide shows you how to build a production-ready system using Pinecone. Why

Chaos Engineering for AI Overview Testing AI system resilience with chaos engineering Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI() class Handler:

AI Computer Use Capabilities: 2025 Guide Overview Building applications with AI that can control computers Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI(

Foundation Models for Robotics: 2025 Guide Overview Applying LLMs and VLMs to robotic control systems Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI() cl

Advanced guide to Hugging Face Transformers including custom Trainer configurations, efficient training with gradient checkpointing, PEFT techniques, and deployment with Inference Endpoints.

提供系统化的AI成本优化方案，包括提示词压缩、语义缓存、智能模型路由、批量处理和Token预算管理，帮助团队在保证质量的前提下大幅降低AI服务成本。

AI Meta-Learning: Advanced Guide Overview Learning to learn: rapid model adaptation patterns. This comprehensive guide covers everything you need to know for production implementation. Why It Matters AI Meta-Learning: Advanced Guide is increasing

Master PyTorch Lightning for production deep learning including multi-GPU training, mixed precision, gradient accumulation, callbacks, and integration with experiment tracking tools.

深入介绍特征存储（Feature Store）的架构设计，包括在线/离线双存储、特征版本控制、时间点正确性（Point-in-Time Correctness）和特征共享，帮助团队消除训练-服务偏差。

AI Failover Strategies Overview Automatic failover between AI providers and regions Implementation ```python from openai import OpenAI from pydantic import BaseModel from typing import Optional import json client = OpenAI() class Handler: "

ONNX Model Optimization Overview Converting and optimizing models for cross-platform deployment. This guide covers practical implementation for production ML systems. Why This Matters in MLOps Modern ML systems require rigorous operations practic

详细介绍在Kubernetes集群上部署LLM推理服务的完整方案，包括GPU节点配置、NVIDIA device plugin、资源配额管理、水平扩缩容（HPA）和跨区域高可用部署。

介绍针对LLM应用的红队测试方法，包括提示词注入攻击、越狱尝试、数据提取和多轮操控，以及防御策略的有效性评估和安全加固建议。

介绍主流AI评估框架的使用方法，包括RAGAS评估RAG质量、DeepEval端到端测试、HELM基准评测和LLM-as-Judge方法，帮助团队建立可靠的AI质量保证体系。

Merging Fine-tuned Models Overview Combining multiple LoRA adapters with model merging. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acceler

Implement AIOps practices including ML-powered anomaly detection, automated root cause analysis, predictive capacity planning, and self-healing infrastructure for modern cloud environments.

深入介绍AI模型推理优化的核心技术，包括vLLM的PagedAttention、TensorRT量化加速、动态批处理、推测解码和模型并行，以及不同场景下的优化策略选择。

Vector Similarity Explained: Technical Deep Dive Overview Cosine similarity, dot product, and Euclidean distance. This comprehensive guide covers everything you need to know for production implementation. Why It Matters Vector Similarity Explaine

Compositional AI Workflows: Advanced Guide Overview Chaining specialized AI components for complex tasks. This comprehensive guide covers everything you need to know for production implementation. Why It Matters Compositional AI Workflows: Advanc

LoRA 微调大模型完全指南（2026）：冻结基座、只训低秩适配器，单卡数小时完成；QLoRA 在 4bit 基座上训练适配器。含 PEFT 真实代码、何时该微调(vs 提示/RAG)、数据质量 > 数量的实战要点。

Build a production churn prediction and retention system using machine learning, survival analysis, causal uplift modeling, and automated intervention workflows with measurable ROI.

AI Observability Stack: Production Architecture 2026 Overview **AI Observability Stack** solves the challenge of complete monitoring for production AI systems. This guide covers the design decisions, implementation details, and trade-offs you need

Dataset Preparation for Fine-tuning Overview Building high-quality fine-tuning datasets from scratch. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets pe