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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
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MLOps in Production: Complete Deployment Guide for Machine Learning Systems in 2025
Build reliable ML pipelines with feature stores, model registries, A/B testing, and automated retraining
Deploying ML models to production is 90% of the work. This comprehensive MLOps guide covers feature engineering pipelines, model training workflows, experiment tracking with MLflow, model registry management, blue-green and canary deployments, automated retraining triggers, monitoring for data drift and model degradation, and building ML platform infrastructure that scales from startup to enterprise.
Deploying AI Models at Scale with Kubernetes: Complete MLOps Guide
KServe, Seldon, autoscaling, canary deployments, and GPU resource management
Kubernetes 规模化部署 AI 模型 MLOps 指南（2026）：KServe/Seldon/vLLM-on-K8s 服务框架、GPU 调度、按 GPU 利用率/队列深度自动扩缩、金丝雀发布、冷启动与多区域，含 KServe InferenceService YAML 与可观测要点。
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。
Neural Architecture Search and AutoML for AI Engineers
Automate model selection and hyperparameter optimization
Learn to use Neural Architecture Search (NAS) and AutoML tools to automatically find optimal model architectures. Covers Optuna, Ray Tune, AutoGluon, and H2O AutoML for practical applications.
AI Model Compression: Pruning, Quantization, and Knowledge Distillation
Deploy smaller, faster AI models without sacrificing accuracy
Learn model compression techniques to make AI models 10x smaller and faster. Covers weight pruning, quantization (INT8, INT4), knowledge distillation, and deployment on edge devices.
High-Performance AI Model Serving with Triton and vLLM
Scale LLM inference to thousands of requests per second
Learn to deploy AI models for high-throughput inference using NVIDIA Triton and vLLM. Covers batching strategies, continuous batching, tensor parallelism, and production serving optimization.
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.
AI-Powered DevOps: Automated CI/CD and Incident Response
Use AI to accelerate software delivery and reduce incidents
Learn to integrate AI into your DevOps pipeline for automated code review, predictive deployment risk, incident detection, and automated remediation. Build smarter CI/CD workflows with AI assistance.
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.
AI in A/B Testing: Statistical Experimentation for ML Systems
Run rigorous experiments to improve AI model performance
Learn to design and analyze experiments for AI systems including shadow testing, canary deployments, multi-armed bandits, and Bayesian A/B testing frameworks for production ML models.
ML Model Versioning and Registry: Production Model Lifecycle Management
MLflow Model Registry, model cards, staging environments, and automated deployment
Implement robust ML model lifecycle management using MLflow Model Registry, covering model versioning, staging environments, approval workflows, and automated deployment pipelines.
AI Production Incident Response: Debugging ML Systems in Production
Runbooks, root cause analysis, and systematic debugging for AI system failures
Build systematic incident response processes for AI systems including runbooks for common failure modes, root cause analysis frameworks, rollback procedures, and post-incident learning.
AI Observability: Comprehensive Monitoring for Production LLM Applications
Langfuse, Helicone, and custom observability stacks for LLM debugging and optimization
Build comprehensive observability for production LLM applications using Langfuse, Helicone, and Prometheus, covering trace collection, metric dashboards, alerting, and cost monitoring.
MLOps Best Practices 2025: From Experimentation to Production ML
MLflow, DVC, CI/CD for ML, feature stores, and model monitoring in practice
Comprehensive MLOps guide covering experiment tracking with MLflow, data versioning with DVC, CI/CD pipelines for ML, feature store integration, and production model monitoring.