book

Machine Learning Design Patterns

Name: Machine Learning Design Patterns
ISBN: 9781098115784

by Valliappa Lakshmanan, Sara Robinson, Michael Munn

October 2020

Intermediate to advanced

405 pages

11h 53m

English

O'Reilly Media, Inc.

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Preface
Who Is This Book For?What’s Not in the BookCode SamplesConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
1. The Need for Machine Learning Design Patterns
What Are Design Patterns?How to Use This BookMachine Learning TerminologyModels and FrameworksData and Feature EngineeringThe Machine Learning ProcessData and Model ToolingRolesCommon Challenges in Machine LearningData QualityReproducibilityData DriftScaleMultiple ObjectivesSummary
2. Data Representation Design Patterns
Simple Data RepresentationsNumerical InputsCategorical InputsDesign Pattern 1: Hashed FeatureProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 2: EmbeddingsProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 3: Feature CrossProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 4: Multimodal InputProblemSolutionTrade-Offs and AlternativesSummary
3. Problem Representation Design Patterns
Design Pattern 5: Reframing ProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 6: Multilabel ProblemSolutionTrade-Offs and AlternativesDesign Pattern 7: EnsemblesProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 8: Cascade ProblemSolutionTrade-Offs and AlternativesDesign Pattern 9: Neutral Class ProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 10: Rebalancing ProblemSolutionTrade-Offs and AlternativesSummary
4. Model Training Patterns
Typical Training LoopStochastic Gradient DescentKeras Training LoopTraining Design PatternsDesign Pattern 11: Useful OverfittingProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 12: CheckpointsProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 13: Transfer LearningProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 14: Distribution StrategyProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 15: Hyperparameter TuningProblemSolutionWhy It WorksTrade-Offs and AlternativesSummary
5. Design Patterns for Resilient Serving
Design Pattern 16: Stateless Serving FunctionProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 17: Batch ServingProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 18: Continued Model EvaluationProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 19: Two-Phase PredictionsProblemSolutionTrade-Offs and AlternativesDesign Pattern 20: Keyed PredictionsProblemSolutionTrade-Offs and AlternativesSummary
6. Reproducibility Design Patterns
Design Pattern 21: TransformProblemSolutionTrade-Offs and AlternativesDesign Pattern 22: Repeatable SplittingProblemSolutionTrade-Offs and AlternativesDesign Pattern 23: Bridged SchemaProblemSolutionTrade-Offs and AlternativesDesign Pattern 24: Windowed InferenceProblemSolutionTrade-Offs and AlternativesDesign Pattern 25: Workflow PipelineProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 26: Feature StoreProblemSolutionWhy It WorksTrade-Offs and AlternativesDesign Pattern 27: Model VersioningProblemSolutionTrade-Offs and AlternativesSummary
7. Responsible AI
Design Pattern 28: Heuristic BenchmarkProblemSolutionTrade-Offs and AlternativesDesign Pattern 29: Explainable PredictionsProblemSolutionTrade-Offs and AlternativesDesign Pattern 30: Fairness LensProblemSolutionTrade-Offs and AlternativesSummary
8. Connected Patterns
Patterns ReferencePattern InteractionsPatterns Within ML ProjectsML Life CycleAI ReadinessCommon Patterns by Use Case and Data TypeNatural Language UnderstandingComputer VisionPredictive AnalyticsRecommendation SystemsFraud and Anomaly Detection
Index

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Machine Learning Design Patterns

by Valliappa Lakshmanan, Sara Robinson, Michael Munn

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