AirSOTA
Air School of Thoughts AtoZAirSOTA 知识矩阵：聚合大模型算法架构、科学育儿情境成长、加州地产考牌实战与全球数字化商业出海的权威专栏。
TalentMe · AI 学习与系统架构
工业级 AI 算法核心 69 题、前沿大模型系统架构演进与北美技术面试全流程备考深度长文。
MLE Data & Feature Engineering: Quality Pipelines, Categorical Encoding, Imbalance, Selection & Drift Detection
> **Core Executive Summary**: Model performance is decided before training starts — by data quality and feature engineering. This guide covers the full MLE data
GPU Hardware Architecture: SM, Tensor Cores, HBM Bandwidth & Roofline Model
> **Core Executive Summary**: AI LLM performance relies directly on underlying GPU hardware physics. Modern GPUs like NVIDIA H100/A100 feature massively paralle
Open & Commercial SOTA LLM Evolution: From BERT/GPT-4 to LLaMA-3, Qwen-3, Gemma-4 & Kimi-K2
> **Core Executive Summary**: Since the Transformer paper, Large Language Models (LLMs) evolved from unidirectional/bidirectional encoders (BERT/GPT-1/2) to lar
Optimization & Matrix Calculus: Lagrange Multipliers, KKT Conditions, SVD & Convergence Geometry
> **Core Executive Summary**: Every machine learning model—from SVM geometric margin maximization to Transformer gradient descent updates—is fundamentally an **
Multimodal Generative System Design: Image/Video Generation & GPU Scaling
> **Core Executive Summary**: Multimodal generative workloads — text-to-image, text-to-video, and vision-language understanding — are the most compute-hungry an
MLE Model Evaluation & Debugging Engineering: CV Strategies, Data Leakage, Bias-Variance Diagnosis, Drift & A/B Validation
> **Core Executive Summary**: A model is only as good as the evaluation loop that validates it. This guide builds the complete evaluation-and-debugging engineer
High-Concurrency AI System Design: SSE Streaming, Semantic Cache & ML Runtimes
> **Core Executive Summary**: Traditional web servers handle millisecond HTTP requests. LLM serving involves multi-second streaming responses. **High-Concurrenc
Mixture-of-Experts (MoE) & DeepSeek MLA/MTP/mHC Architecture: Top-k Routing, Aux-Loss-Free, KAN vs MLP
> **Core Executive Summary**: As model scales reach trillion-parameter frontiers, Dense forward FLOPs become unsustainable. **Mixture-of-Experts (MoE)** replace
AI Math Foundations: Bayes Inference, Shannon Entropy, Cross-Entropy & KL Divergence
> **Core Executive Summary**: Probability theory and information theory form the mathematical backbone of artificial intelligence. From **Bayesian Inference** p
Industry Recommendation System Design: 3-Stage Pipeline, Two-Tower Models & Feature Store
> **Core Executive Summary**: No single model can score a hundred-million-item corpus within a ~50ms latency SLA. Production systems decompose inference into a
MLE System Design Guide: Recommendation, Search & Risk Control
> **Executive Summary**: Machine Learning System Design separates senior Machine Learning Engineers (MLE) and AI Architects from junior modelers. Candidates mus
KV Cache Management: Exact Bounds Derivation, vLLM PagedAttention & Prefix Caching
> **Core Executive Summary**: Autoregressive LLM generation requires caching key-value states to eliminate $O(N^2)$ recomputation. However, **KV Cache** imposes
Classical NLP Tasks: NER, Text Classification, seq2seq Translation & NLI Entailment
> **Core Executive Summary**: Classical NLP established the foundations of text sequence modeling prior to large language models. From **NER (Named Entity Recog
Sampling & Monte Carlo Methods: Inverse Transform, Rejection, Importance Sampling, MCMC & Bootstrap
> **Core Executive Summary**: Sampling theory answers a fundamental question — how do we draw random values from a target distribution when we can only cheaply
Real-time Risk Control & Fraud Detection System Design: Streaming & Graph Risk
> **Core Executive Summary**: A financial-grade risk control system must return a **Pass / Reject / Manual-Review** decision within a **10ms SLA** while scannin