کتاب یادگیری یادگیری خودکار ماشین

یادگیری ماشین خودکار چیست؟

The Growing Demand for Machine Learning Solutions • Addressing the Data Science Talent Gap • Democratizing AI Development • AutoML in the Machine Learning Landscape • Open Source AutoML Libraries • Enterprise AutoML Platforms • Comparison of Leading Frameworks • Who Should Use AutoML? • AutoML Across Industries: Transforming Business Processes • Finance • Healthcare and Life Sciences • Retail and Ecommerce • Manufacturing • Other Sectors • The Tiered Use Case Model • Overcoming Hurdles: Persistent Challenges in AutoML • Interpretability (the “Black Box” Problem) • Need for Customization Versus Automation • Data Quality Dependency and Robustness • Computational Costs and Resource Intensity • Addressing Bias and Fairness • Scalability and Efficiency • The Horizon: Future Trends Shaping AutoML • Synergy with Large Language Models (LLMs) and Foundation Models • Next-Generation Neural Architecture Search (NAS) • Maturity of Multimodal Explainable AI (MXAI) • Continued Democratization via Low-Code/No-Code • Expansion to Edge Computing and Federated Learning • Summary

ظهور و وضعیت فعلی AutoML

Early Automation (Pre-2010): Laying the Groundwork • Feature Selection • Hyperparameter Search • Meta-Learning Research • Limitations of Early Approaches • First Generation (2010–2015): Solving the CASH Problem • Auto-WEKA (2013) • Hyperopt (2013) • SMAC (Sequential Model-Based Algorithm Configuration) • First Generation Impact and Legacy • Second Generation (2015–2020): Solving the Usability and Enterprise Problem • Auto-sklearn (2015) • PyCaret (2020) • TPOT (Tree-Based Pipeline Optimization Tool) (2016) • H2O AutoML • Google Cloud AutoML (Now Part of Vertex AI) • Second Generation Impact and Legacy • Third Generation (2020–Present): Solving the Multimodal and MLOps Problem • AutoGluon (Amazon) • Google Vertex AI • MLJAR and AWS SageMaker Autopilot • Third Generation Key Capabilities • The Emergence of LLM-Assisted AutoML • Summary

درک خط لوله AutoML

The Architecture of Automated Machine Learning • Data Preprocessing • Data Quality Assessment and Cleaning • Missing Data Strategies • Data Validation and Integrity Checks • Feature Engineering • Multilevel Feature Generation • Domain-Specific Feature Engineering • Feature Selection and Pruning • Representation Learning Integration • Hyperparameter Optimization • Advanced Search Strategies • Multifidelity Optimization • Configuration Space Design • Budget-Aware Optimization • Neural Architecture Search • Search Space Engineering • Efficiency Techniques • Hardware-Aware Architecture Search • Architecture Transfer and Meta-Learning • Model Selection, Ensembling, and Stacking • Diversity-Driven Ensemble Construction • Advanced Stacking Techniques • Dynamic Ensemble Selection • Resource-Aware Ensemble Optimization • Model Deployment and Monitoring • Production Readiness Considerations • Scalability and Performance Optimization • Model Monitoring and Maintenance • Interpretability and Explainability • Pipeline Integration and Optimization • Cross-Stage Optimization Strategies • Resource Allocation and Management • Feedback Mechanisms and Continuous Learning • Challenges and Future Directions • Scalability and Efficiency • Robustness and Reliability • Democratization and Accessibility • The Double-Edged Sword of Democratization • Summary

پیش‌پردازش خودکار داده و مهندسی ویژگی

Working Dataset: RetailMart Ecommerce Platform • Intelligent Data Profiling and Quality Assessment • Smart Data Type Handling and Transformation • DateTime Feature Extraction • Text Preprocessing Pipelines • Automated Feature Engineering • Traditional Feature Engineering Automation • Advanced Feature Learning Techniques • Intelligent Feature Selection and Dimensionality Management • Preprocessing Complex and Multimodal Data • Production-Ready Preprocessing Pipelines • Summary

بهینه‌سازی ابرپارامتر

The Challenge of Hyperparameter Optimization • The Computational Cost Challenge • The Sensitivity Problem • Real-World Impact • Grid Search Versus Random Search: Building the Foundation • Grid Search: Systematic but Limited • Random Search: A Surprisingly Effective Alternative • A Practical Comparison • Modern Implementation • When to Use Each Approach • Limitations of Both Approaches • Bayesian Optimization: Learning from Experience • The Core Insight • Surrogate Models • Acquisition Functions • Real-World Success Stories • Modern Tools and Implementation • AWS SageMaker Automatic Model Tuning • Advanced Techniques • Practical Considerations • Limitations and Challenges • Early Stopping and Scheduling: Working Smarter, Not Harder • The Core Insight • Successive Halving: A Tournament Approach • Hyperband: Automating the Resource Allocation • Asynchronous Successive Halving (ASHA) • Population-Based Training: Evolution During Training • Layer Freezing: A Novel Fidelity Dimension • Practical Implementation • Real-World Results • Combining with Bayesian Optimization • When Early Stopping Works Best • A Cautionary Note • Multifidelity Optimization: Beyond Simple Early Stopping • The Multifidelity Paradigm • Advanced Multifidelity Methods • Practical Implementation • Case Study: Personal Investment Portfolio Optimization with Multifidelity HPO • Background and Problem Definition • Dataset and Features • Multifidelity Strategy Implementation • Implementation • Resource Management and Results • Key Insights and Practical Considerations • Early Stopping Effectiveness • Model Type Performance Patterns • Resource Allocation Strategy • Production Deployment Considerations • Lessons for Individual Practitioners • When to Use Multifidelity Optimization • Summary

جستجوی معماری عصبی (NAS)

Understanding Neural Architecture Search • The Three Pillars of NAS • Search Space Design: Defining the Boundaries • The Art of Constraint • Types of Search Spaces • Task-Specific Considerations • Emerging Specialized Search Spaces • Multi-Objective Search Spaces • Balancing Efficiency and Discovery • The NAS-Bench Revolution • Search Strategies: Finding Needles in Haystacks • The Evolution of Search Strategies • Choosing Your Search Strategy: A Practical Perspective • Reinforcement Learning: The Original Approach • Evolutionary Algorithms: Nature-Inspired Search • Differentiable NAS: The Game Changer • Gradient-Based Methods and Advanced Techniques • Hybrid Approaches: Best of Both Worlds • Choosing the Right Strategy • Performance Estimation: The Efficiency Imperative • Bridging the Evaluation Gap • The Training Bottleneck • Multifidelity Evaluation: Training Less, Learning More • One-Shot Architecture Search: Train Once, Evaluate Many • Learning Curve Extrapolation • Zero-Cost Proxies: Instant Architecture Evaluation • Surrogate Models: Learning to Predict Performance • Combining Approaches for Maximum Efficiency • Efficient NAS: Making It Practical • The Efficiency Revolution • Building Production-Ready NAS Systems • Weight Sharing: The Foundation of Efficient NAS • Once-For-All Networks: Decoupling Training and Deployment • Progressive Search Strategies • Hardware-Aware Optimization • Production Deployment Tools for NAS-Discovered Architectures • Zero-Cost Proxies for Rapid Filtering • Practical Implementation Guidelines • Practical Applications and Tools • NAS in the Real World: Integration and Deployment • AutoKeras: Simplicity First • NNI: Enterprise-Grade NAS • Ray Tune + Optuna: Flexible and Powerful • Industry Success Stories • From Notebook to Production: Next Steps • Summary

AutoGluon برای داده‌های جدولی

Setting Up AutoGluon and Environment • Installation Options • Platform-Specific Notes • Setting Up Your Development Environment • Performance Considerations • Cloud Environment Recommendations • Choosing the Right AutoML Framework for Tabular Data • TabularPredictor Basics • Loading and Exploring Data • Basic Model Training • Understanding TabularPredictor Output • Different Prediction Methods • Binary and Multiclass Classification • Binary Classification in Detail • Multiclass Classification • Regression Tasks • Regression Versus Classification Differences • Interpreting Regression Performance • Customizing Basic Behavior • AutoGluon’s Automatic Data Processing • Automatic Feature Type Detection • Missing Value Handling • Categorical Encoding • Advanced Customization • Custom Hyperparameters • Advanced Ensemble Configuration • Feature Engineering Control • Training Process Optimization • Model Interpretability and Debugging • Interpretability Tools • Handling Special Data Scenarios • When to Use Advanced Customization • Project: Titanic Survival Prediction • Project Overview and Business Context • Data Exploration and Understanding • Baseline AutoGluon Model • Custom Feature Engineering for Titanic • Model Interpretation for Titanic • Performance Evaluation and Comparison • Model Deployment Preparation • Project Summary and Business Impact • Extending This Project • Data Pipeline Consistency • Monitoring and Maintaining Models in Production • Monitoring Practices • Monitoring Tools for AutoGluon • Summary

AutoML برای متن و پردازش زبان طبیعی

AutoGluon’s MultiModalPredictor for Text Processing • Why MultiModalPredictor? • Underlying Model Architectures • Real-World Performance • Building Text Classification Models • Your First Text Classification Model • Understanding Model Selection • Hyperparameter Optimization Guidelines • Advanced Text Processing Capabilities • Beyond Classification: Advanced NLP Tasks • The Transformer Revolution and Beyond • Domain-Specific Considerations • Model Selection for Different Use Cases • Maximum Accuracy Applications • Balanced Applications • High-Throughput Applications • Real-World Applications and Performance • Industry Case Studies • Performance Insights • Production Deployment Considerations • Model Selection for Deployment Scenarios • Managed Services Versus Custom Models • Deploying Custom Models with SageMaker • Monitoring and Maintenance • Performance and Operational Monitoring • Data Drift Detection • Retraining and Continuous Improvement • Practical Project: News Article Classification • Summary

پیش‌بینی سری‌های زمانی با AutoGluon

Understanding the Time Series Challenge • Getting Started with TimeSeriesPredictor • Foundation Models and Zero-Shot Forecasting • The Chronos-Bolt Architecture • Real-World Impact of Zero-Shot Forecasting • Handling Complex Multiseries Scenarios • Advanced Capabilities: Covariate Regressors • Implementing Covariate Regressors • Business Impact of Covariate Integration • Model Selection and Hyperparameter Optimization • The Model Zoo • Preset Configurations • Custom Hyperparameter Configuration • Evaluation and Validation Strategies • Backtesting and Time-Aware Validation • Business-Relevant Metrics • Production Deployment and Cloud Integration • AWS Deployment Options • Model Updating and Monitoring • Practical Project: Retail Demand Forecasting • Data Preparation and Exploration • Model Training with Advanced Features • Business Impact Analysis • Future Directions and Emerging Capabilities • Summary

بینایی ماشین با AutoGluon

Understanding AutoGluon’s Computer Vision Capabilities • Choosing Between Custom Models and Managed Services • Building Training Datasets with SageMaker Ground Truth • The MultiModalPredictor Advantage • Foundation Models Integration • Modern Computer Vision Architectures • Task Categories and Applications • Setting Up AutoGluon for Computer Vision • Installation and Environment Setup • Hardware Considerations • Verification and Basic Setup • Image Classification with MultiModalPredictor • Your First Image Classification Model • Understanding Data Formats and Preprocessing • Model Architecture Selection and Presets • Advanced Classification Techniques • Object Detection with AutoGluon • Understanding Object Detection • Basic Object Detection Setup • Enhanced Object Detection Capabilities • Advanced Object Detection Applications • Multimodal Computer Vision Applications • Combining Images with Tabular Data • Image and Text Integration • Real-World Computer Vision Project: Automated Ecommerce Product Classification • Project: Automated Ecommerce Product Classification • Data Preparation and Exploration • Building the Multimodal Classification System • Performance Analysis and Model Interpretability • Integration with Ecommerce Systems • Performance Optimization and Best Practices • Hardware Optimization Strategies • Model Monitoring and Maintenance • Production Deployment Considerations • Model Versioning and Updates • SageMaker Endpoint Deployment • SageMaker Serverless Inference for Cost-Effective Deployment • AWS Panorama for Edge Deployment • Scalable Batch Processing Service • Summary

یکپارچه‌سازی جریان کاری با ابزارهای MLOps

Understanding the AutoML-MLOps Integration Landscape • The Scale Challenge • The Reproducibility Imperative • Experiment Tracking and Model Management • Hierarchical Experiment Organization • Artifact Management Strategies • Workflow Orchestration with Kubeflow • Designing AutoML-Aware Pipelines • Resource Management and Optimization • Production Deployment Patterns • Automated Validation and Quality Assurance • Dynamic Serving Infrastructure • Operational Monitoring and Maintenance • Monitoring and Governance • AutoML-Specific Monitoring Requirements • Governance and Compliance Frameworks • Integration Challenges and Solutions • The Artifact Explosion Challenge • Ensuring Reproducibility in Automated Systems • Bridging Technical and Business Domains • Best Practices and Implementation Guidelines • Building Progressive Capability • Alignment and Expectation Management • Risk Management and Parallel Systems • Observability as a Foundation • Organizational Learning and Adaptation • Summary

اتوماسیون خط لوله داده با Apache Airflow

Understanding Data Pipeline Requirements for AutoML • Airflow Architecture for Machine Learning Workflows • Core Components • Key Airflow Terminology • Designing DAGs for AutoML Data Ingestion • Practical Example: Complete AutoML Data Ingestion DAG • DAG Initialization and Configuration • Understanding catchup Behavior • Dynamic Task Mapping for Parallel Processing • Feature Engineering Pipelines and Feature Stores • Handling Late-Arriving Data • Data Contracts and Schema Evolution • Monitoring and Data Quality Gates • Scaling Airflow for Enterprise AutoML • Operational Excellence and Best Practices • Summary

استقرار و تحویل مستمر برای AutoML

The Unique Challenges of AutoML Deployment • Continuous Integration for Machine Learning • Shadow Deployment Validation • Continuous Deployment Pipelines • Testing Strategies for Automated Models • Contract Testing • Property-Based Testing • Metamorphic Testing • Adversarial Testing • Model Packaging and Containerization • Practical Example: Deploying the Adult Income Prediction Model • Model Serving Infrastructure • Monitoring and Observability in Production • Prometheus–Grafana Monitoring Stack • Drift Detection with Evidently • Security and Compliance Considerations • Input Sanitization and DoS Prevention • Adversarial Attack Defenses • Continuous Learning and Feedback Loops • Summary

مطالعه موردی ۱: خدمات مالی—تشخیص تقلب در زمان واقعی در GlobalBank

Business Problem and Context • Success Criteria • Data Pipeline and Preparation • Data Pipeline Architecture • Production Data Pipeline Considerations • Feature Engineering • 1. Temporal Features: Fraud Has a Schedule • 2. Velocity Features: Fraudsters Move Fast • 3. Behavioral Deviation: Detecting the Unusual • 4. Merchant Risk Scoring • 5. Device Trust • Feature Impact Summary • Model Development with AutoGluon • Sample Weighting for Cost-Sensitive Learning • AutoGluon Configuration • Why PR-AUC Instead of ROC-AUC? • Model Training Results • Model Evaluation and Interpretability • Finding the Optimal Threshold • Model Interpretability with SHAP • Deployment Architecture • FastAPI Inference Service • Graceful Degradation Strategy • Monitoring and Maintenance • Drift Detection with PSI • Automated Retraining Pipeline • A/B Testing for Model Updates • Outcomes and Lessons Learned • Performance Metrics • Business Impact • Key Lessons • The Right Division of Labor • Summary

مطالعه موردی ۲: خرده‌فروشی—پیش‌بینی تقاضای همه‌کاناله

Business Problem and Context • The Scale Challenge • The Wake-Up Call • Project Objectives • Data Challenges: Multisource Integration • Point-of-Sale Data • Ecommerce Data • Inventory Data • Marketing and Promotions Data • Weather Data • External Signals • Data Pipeline Architecture • Key Data Decisions • Feature Engineering: Capturing Demand Drivers • Business-Aligned Metrics • Temporal Features (Baseline) • Omnichannel Behavioral Features (High Impact) • Weather-Driven Demand (Category-Specific) • Promotional Features (Complex Interactions) • Event-Driven Demand • SKU-Specific Attributes • Model Development: AutoGluon for Time Series at Scale • The AutoML Approach • Why Tabular AutoML for Time Series? • Training Strategy: Time-Based Splits • Multihorizon Forecasting • AutoGluon Configuration • Key Configuration Decisions • Handling Data Sparsity (Long-Tail SKUs) • Training Infrastructure • Evaluation: Business Metrics over Model Metrics • Model Performance (MAPE by Forecast Horizon) • Weighted Average MAPE (Business-Aligned) • Business Impact Metrics • Category-Specific Performance • Promotional Forecast Accuracy • Forecast Bias Analysis • Deployment: Production Forecasting Pipeline • Pipeline Architecture • Technology Stack • Forecast Serving • Monitoring: Keeping Forecasts Accurate • Drift Detection • Business Outcomes and Lessons Learned • Quantified Business Impact (12 Months Postlaunch) • Unexpected Benefits • Critical Success Factors • What We’d Do Differently • Lessons for Your Demand Forecasting Project • When AutoML Excels for Demand Forecasting • Summary

مطالعه موردی ۳: مراقبت‌های بهداشتی—پیش‌بینی بازگشت بیمار

The Business Challenge • The Hardest Constraint: Fairness • The Current State • Project Objectives • Data Challenges and HIPAA Compliance • Data Sources and Integration • Missing Data Patterns • Data Quality Issues • Feature Engineering: Structured and Unstructured Data • Category 1: Demographics and Social Determinants (42 Features) • Category 2: Clinical Complexity and Comorbidities (68 features) • Category 3: Utilization History (53 features) • Category 4: Current Encounter Features (87 features) • Category 5: Clinical Notes Embeddings (64 features) • Category 6: Temporal and Interaction Features (33 features) • Model Development: Fairness-Aware AutoML • The Fairness Challenge • Fairness Metrics Defined • Baseline Model: Standard AutoGluon (No Fairness Constraints) • Approach 1: Remove Protected Attributes • Approach 2: Adversarial Debiasing • Approach 3 (Final): Fairness-Aware Ensemble with Reweighting • Final Model Configuration • Evaluation: Performance + Fairness Metrics • Model Performance (Overall) • Fairness Metrics by Race • Fairness by Age Group • Feature Importance (Top 20 by SHAP) • Business Metrics • Deployment: Clinical Workflow Integration • Real-Time Prediction Architecture • EHR Integration (Epic) • Clinical Decision Support Alert • Care Management Workflow • Clinician Training and Change Management • Interpretability for Clinicians • Regulatory Considerations • Monitoring: Drift and Fairness • Three-Layer Monitoring Strategy • Retraining Schedule • Business Outcomes and Lessons Learned • Clinical Outcomes • Fairness in Practice • Unexpected Benefits • Critical Success Factors • What We’d Do Differently • Lessons for Your Readmission Project • When AutoML Excels for Healthcare • The Production AutoML Blueprint: A Grand Synthesis • The Universal Patterns of Production AutoML • The Production Readiness Checklist • Final Thoughts • Summary