Tag: architecture
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多臂老虎机与在线决策：MAB 探索与利用、LinUCB、Thompson Sampling 与 Contextual Bandits 落地
> **核心摘要**：在推荐系统、在线广告投递与搜索重排中，系统面临着经典的**探索与利用 (Exploration vs Exploitation)** 矛盾——是继续推荐过去表现优异的旧物品（Exploitation），还是推荐潜在高收益的新冷启动物品（Exploration）？**多臂老虎机 (Multi-Arm
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实时风控与欺诈检测系统架构：流批一体、图风控与实时特征工程
> **核心摘要**：金融级风控系统必须在 **10ms 决策 SLA** 内对每秒数百万级事件给出 **放行 / 拒绝 / 人工复核** 三种决策,而欺诈率往往低于 **0.1%**。本指南全量拆解实时风控链路——事件接入、实时特征工程、规则引擎、模型打分、策略决策与人工审核——并深入异常检测、样本不平衡与代价敏感学
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DS Causal Inference: PSM, Difference-in-Differences & Synthetic Control
> **Executive Summary**: Causal inference separates senior Data Scientists and Product Analysts from junior data query roles. In real-world tech systems, ethica
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RS Core Cheatsheet: Top 30 Papers Breakdown & Deep RL
> **Executive Summary**: Technical interviews for Research Scientist (RS) roles evaluate first-principles mathematical rigor, analytical loss derivations, gener
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Prompt Engineering & Safety Guardrails: Outlines & Llama Guard
> **Core Executive Summary**: In enterprise AI application development, prompt engineering is far more than Few-Shot examples or Chain-of-Thought instructions.
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MLOps & Online Testing: Data Drift Monitoring, PSI Metric, A/B Testing & CUPED
> **Core Executive Summary**: Production deployment is not the end of the ML lifecycle. **MLOps & LLMOps** maintain real-time observability, continuous retraini
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Normalization & Regularization Taxonomy: BatchNorm, LayerNorm, RMSNorm, L0/L1/L2 Weight Decay & Inverted Dropout Guide
> **Summary**: Normalization and Regularization stabilize training dynamics and prevent overfitting. This 100% exhaustive guide covers feature scaling (Standard
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Parameter-Efficient Fine-Tuning (PEFT): LoRA, QLoRA, DoRA, Prefix/Prompt Tuning, Adapters & MoRA/ReLoRA
> **Core Executive Summary**: As Large Language Models (LLMs) scale to hundreds of billions of parameters, Full Fine-Tuning becomes computationally prohibitive.
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ML Evaluation Metrics & Data Engineering: Classification, Regression, Ranking (NDCG), Calibration & Preprocessing Guide
> **Summary**: Evaluation metrics and preprocessing form the mathematical bridge connecting raw models to real-world business value. This exhaustive guide cover
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Statistical Inference & Hypothesis Testing: Distribution Families, MLE, CLT, p-Values, Confidence Intervals & Power Analysis
> **Core Executive Summary**: Statistical inference is the discipline of turning noisy data into calibrated decisions under uncertainty, and hypothesis testing