Designing Machine Learning Systems

Kup polskie wydanie:

Spis treści książki

• Preface
  • Who This Book Is For
  • What This Book Is Not
  • Navigating This Book
  • GitHub Repository and Community
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Overview of Machine Learning Systems
  • When to Use Machine Learning
    • Machine Learning Use Cases
  • Understanding Machine Learning Systems
    • Machine Learning in Research Versus in Production
      • Different stakeholders and requirements
      • Computational priorities
      • Data
      • Fairness
      • Interpretability
      • Discussion
    • Machine Learning Systems Versus Traditional Software
  • Summary
• 2. Introduction to Machine Learning Systems Design
  • Business and ML Objectives
  • Requirements for ML Systems
    • Reliability
    • Scalability
    • Maintainability
    • Adaptability
  • Iterative Process
  • Framing ML Problems
    • Types of ML Tasks
      • Classification versus regression
      • Binary versus multiclass classification
      • Multiclass versus multilabel classification
      • Multiple ways to frame a problem
    • Objective Functions
      • Decoupling objectives
  • Mind Versus Data
  • Summary
• 3. Data Engineering Fundamentals
  • Data Sources
  • Data Formats
    • JSON
    • Row-Major Versus Column-Major Format
    • Text Versus Binary Format
  • Data Models
    • Relational Model
    • NoSQL
      • Document model
      • Graph model
    • 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
• 4. Training Data
  • Sampling
    • Nonprobability Sampling
    • Simple Random Sampling
    • Stratified Sampling
    • Weighted Sampling
    • Reservoir Sampling
    • Importance Sampling
  • Labeling
    • Hand Labels
      • Label multiplicity
      • Data lineage
    • Natural Labels
      • Feedback loop length
    • Handling the Lack of Labels
      • Weak supervision
      • Semi-supervision
      • Transfer learning
      • Active learning
  • Class Imbalance
    • Challenges of Class Imbalance
    • Handling Class Imbalance
      • Using the right evaluation metrics
      • Data-level methods: Resampling
      • Algorithm-level methods
        • Cost-sensitive learning
        • Class-balanced loss
        • Focal loss
  • Data Augmentation
    • Simple Label-Preserving Transformations
    • Perturbation
    • Data Synthesis
  • Summary
• 5. Feature Engineering
  • Learned Features Versus Engineered Features
  • Common Feature Engineering Operations
    • Handling Missing Values
      • Deletion
      • Imputation
    • Scaling
    • Discretization
    • Encoding Categorical Features
    • Feature Crossing
    • Discrete and Continuous Positional Embeddings
  • Data Leakage
    • Common Causes for Data Leakage
      • Splitting time-correlated data randomly instead of by time
      • Scaling before splitting
      • Filling in missing data with statistics from the test split
      • Poor handling of data duplication before splitting
      • Group leakage
      • Leakage from data generation process
    • Detecting Data Leakage
  • Engineering Good Features
    • Feature Importance
    • Feature Generalization
  • Summary
• 6. Model Development and Offline Evaluation
  • Model Development and Training
    • Evaluating ML Models
      • Six tips for model selection
        • Avoid the state-of-the-art trap
        • Start with the simplest models
        • Avoid human biases in selecting models
        • Evaluate good performance now versus good performance later
        • Evaluate trade-offs
        • Understand your models assumptions
    • Ensembles
      • Bagging
      • Boosting
      • Stacking
    • Experiment Tracking and Versioning
      • Experiment tracking
      • Versioning
    • Distributed Training
      • Data parallelism
      • Model parallelism
    • AutoML
      • Soft AutoML: Hyperparameter tuning
      • Hard AutoML: Architecture search and learned optimizer
  • Model Offline Evaluation
    • Baselines
    • Evaluation Methods
      • Perturbation tests
      • Invariance tests
      • Directional expectation tests
      • Model calibration
      • Confidence measurement
      • Slice-based evaluation
  • Summary
• 7. Model Deployment and Prediction Service
  • Machine Learning Deployment Myths
    • Myth 1: You Only Deploy One or Two ML Models at a Time
    • Myth 2: If We Dont Do Anything, Model Performance Remains the Same
    • Myth 3: You Wont Need to Update Your Models as Much
    • Myth 4: Most ML Engineers Dont 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
      • Model optimization
      • Using ML to optimize ML models
    • ML in Browsers
  • Summary
• 8. Data Distribution Shifts and Monitoring
  • Causes of ML System Failures
    • Software System Failures
    • ML-Specific Failures
      • Production data differing from training data
      • Edge cases
      • Degenerate feedback loops
        • Detecting degenerate feedback loops
        • Correcting degenerate feedback loops
  • Data Distribution Shifts
    • Types of Data Distribution Shifts
      • Covariate shift
      • Label shift
      • Concept drift
    • General Data Distribution Shifts
    • Detecting Data Distribution Shifts
      • Statistical methods
      • Time scale windows for detecting shifts
    • Addressing Data Distribution Shifts
  • Monitoring and Observability
    • ML-Specific Metrics
      • Monitoring accuracy-related metrics
      • Monitoring predictions
      • Monitoring features
      • Monitoring raw inputs
    • Monitoring Toolbox
      • Logs
      • Dashboards
      • Alerts
    • Observability
  • Summary
• 9. Continual Learning and Test in Production
  • Continual Learning
    • Stateless Retraining Versus Stateful Training
    • Why Continual Learning?
    • Continual Learning Challenges
      • Fresh data access challenge
      • Evaluation challenge
      • Algorithm challenge
    • Four Stages of Continual Learning
      • Stage 1: Manual, stateless retraining
      • Stage 2: Automated retraining
        • Requirements
      • Stage 3: Automated, stateful training
        • Requirements
      • Stage 4: Continual learning
        • Requirements
    • How Often to Update Your Models
      • Value of data freshness
      • Model iteration versus data iteration
  • Test in Production
    • Shadow Deployment
    • A/B Testing
    • Canary Release
    • Interleaving Experiments
    • Bandits
      • Contextual bandits as an exploration strategy
  • Summary
• 10. Infrastructure and Tooling for MLOps
  • Storage and Compute
    • Public Cloud Versus Private Data Centers
  • Development Environment
    • Dev Environment Setup
      • IDE
    • 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
• 11. The Human Side of Machine Learning
  • User Experience
    • Ensuring User Experience Consistency
    • Combatting Mostly Correct Predictions
    • Smooth Failing
  • Team Structure
    • Cross-functional Teams Collaboration
    • End-to-End Data Scientists
      • Approach 1: Have a separate team to manage production
      • Approach 2: Data scientists own the entire process
  • Responsible AI
    • Irresponsible AI: Case Studies
      • Case study I: Automated graders biases
        • Failure 1: Setting the wrong objective
        • Failure 2: Insufficient fine-grained model evaluation to discover biases
        • Failure 3: Lack of transparency
      • Case study II: The danger of anonymized data
    • A Framework for Responsible AI
      • Discover sources for model biases
      • Understand the limitations of the data-driven approach
      • Understand the trade-offs between different desiderata
      • Act early
      • Create model cards
      • Establish processes for mitigating biases
      • Stay up-to-date on responsible AI
  • Summary
• Epilogue
• Index

pokaż cały spis treści

ukryj recenzje