Machine Learning Pattern

A Machine Learning Pattern is a design pattern that provides a reusable solution for common machine learning system design challenges.

• AKA: ML Pattern, Learning Pattern, AI Design Pattern.
• Context:
  • It can typically address Machine Learning System Challenges through machine learning proven solutions.
  • It can typically encode Machine Learning Best Practices via machine learning architectural decisions.
  • It can often accelerate Machine Learning System Development through machine learning template reuses.
  • It can often improve Machine Learning System Quality via machine learning tested approaches.
  • It can range from being a Data Machine Learning Pattern to being a Model Machine Learning Pattern, depending on its machine learning focus area.
  • It can range from being a Training Machine Learning Pattern to being a Inference Machine Learning Pattern, depending on its machine learning lifecycle phase.
  • It can range from being a Simple Machine Learning Pattern to being a Composite Machine Learning Pattern, depending on its machine learning structural complexity.
  • It can range from being a Generic Machine Learning Pattern to being a Domain-Specific Machine Learning Pattern, depending on its machine learning application scope.
  • ...
• Examples:
  • Model Machine Learning Patterns, such as:
    • Model Combination Pattern for multi-model coordination.
    • Ensemble Pattern for prediction aggregation.
    • Model Cascade Pattern for efficiency optimization.
  • Data Machine Learning Patterns, such as:
    • Data Augmentation Pattern for training set expansion.
    • Feature Engineering Pattern for representation improvement.
    • Data Pipeline Pattern for preprocessing automation.
  • Training Machine Learning Patterns, such as:
    • Transfer Learning Pattern for knowledge reuse.
    • Curriculum Learning Pattern for progressive training.
    • Active Learning Pattern for sample efficiency.
  • Deployment Machine Learning Patterns, such as:
    • Model Serving Pattern for inference optimization.
    • A/B Testing Pattern for model comparison.
    • Model Versioning Pattern for deployment management.
  • ...
• Counter-Examples:
  • Software Design Pattern, which addresses general software rather than ML-specific challenges.
  • Algorithm, which is a specific procedure rather than a reusable template.
  • Framework, which provides infrastructure rather than design guidance.
• See: Design Pattern, Machine Learning System, Machine Learning Task, Model Architecture, Training Strategy, ML Best Practice, System Design.

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