کتاب طراحی سیستم‌های یادگیری ماشین

نگاهی کلی به سیستم‌های یادگیری ماشین

When to Use Machine Learning • Machine Learning Use Cases • Understanding Machine Learning Systems • Machine Learning in Research Versus in Production • Machine Learning Systems Versus Traditional Software • Summary

مقدمه‌ای بر طراحی سیستم‌های یادگیری ماشین

Business and ML Objectives • Requirements for ML Systems • Reliability • Scalability • Maintainability • Adaptability • Iterative Process • Framing ML Problems • Types of ML Tasks • Objective Functions • Mind Versus Data • Summary

مبانی مهندسی داده

Data Sources • Data Formats • JSON • Row-Major Versus Column-Major Format • Text Versus Binary Format • Data Models • Relational Model • NoSQL • Structured Versus Unstructured Data • Data Storage Engines and Processing • Transactional and Analytical Processing • ETL: Extract, Transform, and Load • Modes of Dataflow • Data Passing Through Databases • Data Passing Through Services • Data Passing Through Real-Time Transport • Batch Processing Versus Stream Processing • Summary

داده‌های آموزشی

Sampling • Nonprobability Sampling • Simple Random Sampling • Stratified Sampling • Weighted Sampling • Reservoir Sampling • Importance Sampling • Labeling • Hand Labels • Natural Labels • Handling the Lack of Labels • Class Imbalance • Challenges of Class Imbalance • Handling Class Imbalance • Data Augmentation • Simple Label-Preserving Transformations • Perturbation • Data Synthesis • Summary

مهندسی ویژگی‌ها

Learned Features Versus Engineered Features • Common Feature Engineering Operations • Handling Missing Values • Scaling • Discretization • Encoding Categorical Features • Feature Crossing • Discrete and Continuous Positional Embeddings • Data Leakage • Common Causes for Data Leakage • Detecting Data Leakage • Engineering Good Features • Feature Importance • Feature Generalization • Summary

توسعه مدل و ارزیابی برون‌خط

Model Development and Training • Evaluating ML Models • Ensembles • Experiment Tracking and Versioning • Distributed Training • AutoML • Model Offline Evaluation • Baselines • Evaluation Methods • Summary

استقرار مدل و سرویس پیش‌بینی

Machine Learning Deployment Myths • Myth 1: You Only Deploy One or Two ML Models at a Time • Myth 2: If We Don’t Do Anything, Model Performance Remains the Same • Myth 3: You Won’t Need to Update Your Models as Much • Myth 4: Most ML Engineers Don’t Need to Worry About Scale • Batch Prediction Versus Online Prediction • From Batch Prediction to Online Prediction • Unifying Batch Pipeline and Streaming Pipeline • Model Compression • Low-Rank Factorization • Knowledge Distillation • Pruning • Quantization • ML on the Cloud and on the Edge • Compiling and Optimizing Models for Edge Devices • ML in Browsers • Summary

تغییرات توزیع داده و پایش

Causes of ML System Failures • Software System Failures • ML-Specific Failures • Data Distribution Shifts • Types of Data Distribution Shifts • General Data Distribution Shifts • Detecting Data Distribution Shifts • Addressing Data Distribution Shifts • Monitoring and Observability • ML-Specific Metrics • Monitoring Toolbox • Observability • Summary

یادگیری مداوم و تست در محیط عملیاتی

Continual Learning • Stateless Retraining Versus Stateful Training • Why Continual Learning? • Continual Learning Challenges • Four Stages of Continual Learning • How Often to Update Your Models • Test in Production • Shadow Deployment • A/B Testing • Canary Release • Interleaving Experiments • Bandits • Summary

زیرساخت و ابزارهای MLOps

Storage and Compute • Public Cloud Versus Private Data Centers • Development Environment • Dev Environment Setup • Standardizing Dev Environments • From Dev to Prod: Containers • Resource Management • Cron, Schedulers, and Orchestrators • Data Science Workflow Management • ML Platform • Model Deployment • Model Store • Feature Store • Build Versus Buy • Summary

جنبه انسانی یادگیری ماشین

User Experience • Ensuring User Experience Consistency • Combatting “Mostly Correct” Predictions • Smooth Failing • Team Structure • Cross-functional Teams Collaboration • End-to-End Data Scientists • Responsible AI • Irresponsible AI: Case Studies • A Framework for Responsible AI • Summary