کتاب سیستم‌های تولید یادگیری ماشین

مقدمه‌ای بر سیستم‌های تولید یادگیری ماشین

What Is Production Machine Learning? • Benefits of Machine Learning Pipelines • Focus on Developing New Models, Not on Maintaining Existing Models • Prevention of Bugs • Creation of Records for Debugging and Reproducing Results • Standardization • The Business Case for ML Pipelines • When to Use Machine Learning Pipelines • Steps in a Machine Learning Pipeline • Data Ingestion and Data Versioning • Data Validation • Feature Engineering • Model Training and Model Tuning • Model Analysis • Model Deployment • Looking Ahead

جمع‌آوری، برچسب‌گذاری و اعتبارسنجی داده‌ها

Important Considerations in Data Collection • Responsible Data Collection • Labeling Data: Data Changes and Drift in Production ML • Labeling Data: Direct Labeling and Human Labeling • Validating Data: Detecting Data Issues • Validating Data: TensorFlow Data Validation • Skew Detection with TFDV • Types of Skew • Example: Spotting Imbalanced Datasets with TensorFlow Data Validation • Conclusion

مهندسی ویژگی و انتخاب ویژگی

Introduction to Feature Engineering • Preprocessing Operations • Feature Engineering Techniques • Normalizing and Standardizing • Bucketizing • Feature Crosses • Dimensionality and Embeddings • Visualization • Feature Transformation at Scale • Choose a Framework That Scales Well • Avoid Training–Serving Skew • Consider Instance-Level Versus Full-Pass Transformations • Using TensorFlow Transform • Analyzers • Code Example • Feature Selection • Feature Spaces • Feature Selection Overview • Filter Methods • Wrapper Methods • Embedded Methods • Feature and Example Selection for LLMs and GenAI • Example: Using TF Transform to Tokenize Text • Benefits of Using TF Transform • Alternatives to TF Transform • Conclusion

سفر داده و ذخیره‌سازی داده

Data Journey • ML Metadata • Using a Schema • Schema Development • Schema Environments • Changes Across Datasets • Enterprise Data Storage • Feature Stores • Data Warehouses • Data Lakes • Conclusion

برچسب‌گذاری پیشرفته، افزایش داده و پیش‌پردازش داده

Advanced Labeling • Semi-Supervised Labeling • Active Learning • Weak Supervision • Advanced Labeling Review • Data Augmentation • Example: CIFAR-10 • Other Augmentation Techniques • Data Augmentation Review • Preprocessing Time Series Data: An Example • Windowing • Sampling • Conclusion

تکنیک‌های مدیریت منابع مدل

Dimensionality Reduction: Dimensionality Effect on Performance • Example: Word Embedding Using Keras • Curse of Dimensionality • Adding Dimensions Increases Feature Space Volume • Dimensionality Reduction • Quantization and Pruning • Mobile, IoT, Edge, and Similar Use Cases • Quantization • Optimizing Your TensorFlow Model with TF Lite • Optimization Options • Pruning • Knowledge Distillation • Teacher and Student Networks • Knowledge Distillation Techniques • TMKD: Distilling Knowledge for a Q&A Task • Increasing Robustness by Distilling EfficientNets • Conclusion

مدل‌سازی با کارایی بالا

Distributed Training • Data Parallelism • Efficient Input Pipelines • Input Pipeline Basics • Input Pipeline Patterns: Improving Efficiency • Optimizing Your Input Pipeline with TensorFlow Data • Training Large Models: The Rise of Giant Neural Nets and Parallelism • Potential Solutions and Their Shortcomings • Pipeline Parallelism to the Rescue? • Conclusion

تحلیل مدل

Analyzing Model Performance • Black-Box Evaluation • Performance Metrics and Optimization Objectives • Advanced Model Analysis • TensorFlow Model Analysis • The Learning Interpretability Tool • Advanced Model Debugging • Benchmark Models • Sensitivity Analysis • Residual Analysis • Model Remediation • Discrimination Remediation • Fairness • Fairness Evaluation • Fairness Considerations • Continuous Evaluation and Monitoring • Conclusion

تفسیرپذیری

Explainable AI • Model Interpretation Methods • Method Categories • Intrinsically Interpretable Models • Model-Agnostic Methods • Local Interpretable Model-Agnostic Explanations • Shapley Values • The SHAP Library • Testing Concept Activation Vectors • AI Explanations • Example: Exploring Model Sensitivity with SHAP • Regression Models • Natural Language Processing Models • Conclusion

جستجوی معماری عصبی

Hyperparameter Tuning • Introduction to AutoML • Key Components of NAS • Search Spaces • Search Strategies • Performance Estimation Strategies • AutoML in the Cloud • Amazon SageMaker Autopilot • Microsoft Azure Automated Machine Learning • Google Cloud AutoML • Using AutoML • Generative AI and AutoML • Conclusion

مقدمه‌ای بر سرویس‌دهی مدل

Model Training • Model Prediction • Latency • Throughput • Cost • Resources and Requirements for Serving Models • Cost and Complexity • Accelerators • Feeding the Beast • Model Deployments • Data Center Deployments • Mobile and Distributed Deployments • Model Servers • Managed Services • Conclusion

الگوهای سرویس‌دهی مدل

Batch Inference • Batch Throughput • Batch Inference Use Cases • ETL for Distributed Batch and Stream Processing Systems • Introduction to Real-Time Inference • Synchronous Delivery of Real-Time Predictions • Asynchronous Delivery of Real-Time Predictions • Optimizing Real-Time Inference • Real-Time Inference Use Cases • Serving Model Ensembles • Ensemble Topologies • Example Ensemble • Ensemble Serving Considerations • Model Routers: Ensembles in GenAI • Data Preprocessing and Postprocessing in Real Time • Training Transformations Versus Serving Transformations • Windowing • Options for Preprocessing • Enter TensorFlow Transform • Postprocessing • Inference at the Edge and at the Browser • Challenges • Model Deployments via Containers • Training on the Device • Federated Learning • Runtime Interoperability • Inference in Web Browsers • Conclusion

زیرساخت سرویس‌دهی مدل

Model Servers • TensorFlow Serving • NVIDIA Triton Inference Server • TorchServe • Building Scalable Infrastructure • Containerization • Traditional Deployment Era • Virtualized Deployment Era • Container Deployment Era • The Docker Containerization Framework • Container Orchestration • Reliability and Availability Through Redundancy • Observability • High Availability • Automated Deployments • Hardware Accelerators • GPUs • TPUs • Conclusion

مثال‌های سرویس‌دهی مدل

Example: Deploying TensorFlow Models with TensorFlow Serving • Exporting Keras Models for TF Serving • Setting Up TF Serving with Docker • Basic Configuration of TF Serving • Making Model Prediction Requests with REST • Making Model Prediction Requests with gRPC • Getting Predictions from Classification and Regression Models • Using Payloads • Getting Model Metadata from TF Serving • Making Batch Inference Requests • Example: Profiling TF Serving Inferences with TF Profiler • Prerequisites • TensorBoard Setup • Model Profile • Example: Basic TorchServe Setup • Installing the TorchServe Dependencies • Exporting Your Model for TorchServe • Setting Up TorchServe • Making Model Prediction Requests • Making Batch Inference Requests • Conclusion

مدیریت و تحویل مدل

Experiment Tracking • Experimenting in Notebooks • Experimenting Overall • Tools for Experiment Tracking and Versioning • Introduction to MLOps • Data Scientists Versus Software Engineers • ML Engineers • ML in Products and Services • MLOps • MLOps Methodology • MLOps Level 0 • MLOps Level 1 • MLOps Level 2 • Components of an Orchestrated Workflow • Three Types of Custom Components • Python Function–Based Components • Container-Based Components • Fully Custom Components • TFX Deep Dive • TFX SDK • Intermediate Representation • Runtime • Implementing an ML Pipeline Using TFX Components • Advanced Features of TFX • Managing Model Versions • Approaches to Versioning Models • Model Lineage • Model Registries • Continuous Integration and Continuous Deployment • Continuous Integration • Continuous Delivery • Progressive Delivery • Blue/Green Deployment • Canary Deployment • Live Experimentation • Conclusion

مانیتورینگ و لاگ‌گیری مدل

The Importance of Monitoring • Observability in Machine Learning • What Should You Monitor? • Custom Alerting in TFX • Logging • Distributed Tracing • Monitoring for Model Decay • Data Drift and Concept Drift • Model Decay Detection • Supervised Monitoring Techniques • Unsupervised Monitoring Techniques • Mitigating Model Decay • Retraining Your Model • When to Retrain • Automated Retraining • Conclusion

حریم خصوصی و الزامات قانونی

Why Is Data Privacy Important? • What Data Needs to Be Kept Private? • Harms • Only Collect What You Need • GenAI Data Scraped from the Web and Other Sources • Legal Requirements • The GDPR and the CCPA • The GDPR’s Right to Be Forgotten • Pseudonymization and Anonymization • Differential Privacy • Local and Global DP • Epsilon-Delta DP • Applying Differential Privacy to ML • TensorFlow Privacy Example • Federated Learning • Encrypted ML • Conclusion

ارکستراسیون پایپ‌لاین‌های یادگیری ماشین

An Introduction to Pipeline Orchestration • Why Pipeline Orchestration? • Directed Acyclic Graphs • Pipeline Orchestration with TFX • Interactive TFX Pipelines • Converting Your Interactive Pipeline for Production • Orchestrating TFX Pipelines with Apache Beam • Orchestrating TFX Pipelines with Kubeflow Pipelines • Introduction to Kubeflow Pipelines • Installation and Initial Setup • Accessing Kubeflow Pipelines • The Workflow from TFX to Kubeflow • OpFunc Functions • Orchestrating Kubeflow Pipelines • Google Cloud Vertex Pipelines • Setting Up Google Cloud and Vertex Pipelines • Setting Up a Google Cloud Service Account • Orchestrating Pipelines with Vertex Pipelines • Executing Vertex Pipelines • Choosing Your Orchestrator • Interactive TFX • Apache Beam • Kubeflow Pipelines • Google Cloud Vertex Pipelines • Alternatives to TFX • Conclusion

TFX پیشرفته

Advanced Pipeline Practices • Configure Your Components • Import Artifacts • Use Resolver Node • Execute a Conditional Pipeline • Export TF Lite Models • Warm-Starting Model Training • Use Exit Handlers • Trigger Messages from TFX • Custom TFX Components: Architecture and Use Cases • Architecture of TFX Components • Use Cases of Custom Components • Using Function-Based Custom Components • Writing a Custom Component from Scratch • Defining Component Specifications • Defining Component Channels • Writing the Custom Executor • Writing the Custom Driver • Assembling the Custom Component • Using Our Basic Custom Component • Implementation Review • Reusing Existing Components • Creating Container-Based Custom Components • Which Custom Component Is Right for You? • TFX-Addons • Conclusion

پایپ‌لاین‌های ML برای مسائل بینایی ماشین

Our Data • Our Model • Custom Ingestion Component • Data Preprocessing • Exporting the Model • Our Pipeline • Data Ingestion • Data Preprocessing • Model Training • Model Evaluation • Model Export • Putting It All Together • Executing on Apache Beam • Executing on Vertex Pipelines • Model Deployment with TensorFlow Serving • Conclusion

پایپ‌لاین‌های ML برای پردازش زبان طبیعی

Our Data • Our Model • Ingestion Component • Data Preprocessing • Putting the Pipeline Together • Executing the Pipeline • Model Deployment with Google Cloud Vertex • Registering Your ML Model • Creating a New Model Endpoint • Deploying Your ML Model • Requesting Predictions from the Deployed Model • Cleaning Up Your Deployed Model • Conclusion

هوش مصنوعی مولد

Generative Models • GenAI Model Types • Agents and Copilots • Pretraining • Pretraining Datasets • Embeddings • Self-Supervised Training with Masks • Fine-Tuning • Fine-Tuning Versus Transfer Learning • Fine-Tuning Datasets • Fine-Tuning Considerations for Production • Fine-Tuning Versus Model APIs • Parameter-Efficient Fine-Tuning • LoRA • S-LoRA • Human Alignment • Reinforcement Learning from Human Feedback • Reinforcement Learning from AI Feedback • Direct Preference Optimization • Prompting • Chaining • Retrieval Augmented Generation • ReAct • Evaluation • Evaluation Techniques • Benchmarking Across Models • LMOps • GenAI Attacks • Jailbreaks • Prompt Injection • Responsible GenAI • Design for Responsibility • Conduct Adversarial Testing • Constitutional AI • Conclusion

آینده سیستم‌های تولید یادگیری ماشین و گام‌های بعدی

Let’s Think in Terms of ML Systems, Not ML Models • Bringing ML Systems Closer to Domain Experts • Privacy Has Never Been More Important • Conclusion