Trained Model

A Trained Model is a parameterized learned software function that can solve model-based prediction tasks.

• AKA: Learned Model, Fitted Model, Trained Statistical Model, Trained ML Model, Trained Machine Learning Model, Statistically Trained Model, Fitted Statistical Model.
• Context:
  • It can typically execute Inference Tasks using learned parameters from training data.
  • It can typically generate Model Outputs from input data based on learned patterns.
  • It can typically maintain Model Weights that encode statistical relationships.
  • It can typically undergo Model Evaluation Tasks to assess model performance metrics.
  • It can typically support Decision-Making Processes through automated inferences.
  • ...
  • It can often require Model Retraining Tasks to adapt to data distribution shifts.
  • It can often undergo Model Optimization Tasks to improve inference efficiency.
  • It can often be compressed through Model Quantization Tasks for edge deployment.
  • It can often integrate into Production Pipelines via model serving infrastructure.
  • ...
  • It can range from being a Simple Trained Model to being a Complex Trained Model, depending on its model architectural complexity.
  • It can range from being a Small Trained Model to being a Large Trained Model, depending on its model parameter count.
  • It can range from being a Task-Specific Trained Model to being a Multi-Task Trained Model, depending on its model capability scope.
  • It can range from being a Shallow Trained Model to being a Deep Trained Model, depending on its model layer depth.
  • It can range from being an Interpretable Trained Model to being a Black-Box Trained Model, depending on its model transparency level.
  • ...
  • It can be produced by a Model Training System that implements training algorithms.
  • It can be serialized by an ML Model Serialization System (such as MLflow, ONNX, MLeap).
  • It can be managed by an ML Model Management System through model lifecycle management.
  • It can be deployed by an ML Model Deployment System (such as AWS SageMaker, TensorFlow Serving).
  • It can be evaluated by a Model Evaluation System using test datasets.
  • It can be versioned by a Model Version Control System for reproducibility.
  • ...
• Example(s):
  • Supervised Trained Models, such as:
    • Trained Classification Models, such as:
      • Trained Decision Tree (trained on iris dataset).
      • Trained Random Forest Model for multi-class classification.
      • Trained Neural Network Classifier for image classification.
      • Trained Support Vector Machine for binary classification.
    • Trained Regression Models, such as:
      • Fitted Linear Regression Model (e.g., LLSE: [math]\displaystyle{ y=1.4x+3.5 }[/math]).
      • Trained Polynomial Regression Model for non-linear relationships.
      • Trained Ridge Regression Model for regularized prediction.
    • Trained Ranking Functions for information retrieval tasks.
  • Unsupervised Trained Models, such as:
    • Trained Clustering Models, such as:
      • Trained K-Means Model for data segmentation.
      • Trained DBSCAN Model for density-based clustering.
    • Fitted Latent Factors Models, such as:
      • Trained PCA Model for dimensionality reduction.
      • Trained Autoencoder for representation learning.
  • Domain-Specific Trained Models, such as:
    • Legal-Domain Trained Models, such as:
      • Trained Legal NER Model for legal entity extraction.
      • Trained Contract Analysis Model for clause-risk identification tasks.
      • Trained Legal Document Classifier for legal document categorization.
      • Trained Case Outcome Predictor for litigation risk assessment.
    • Medical-Domain Trained Models, such as:
      • Trained Disease Diagnosis Model for medical prediction tasks.
      • Trained Medical Image Segmentation Model for radiology analysis.
    • Financial-Domain Trained Models, such as:
      • Trained Credit Scoring Model for risk assessment.
      • Trained Fraud Detection Model for transaction monitoring.
  • Pre-Trained Models, such as:
    • Pre-Trained Language Models, such as:
      • GPT-based Models for text generation tasks.
      • BERT-based Models for text understanding tasks.
      • ELMo Models for contextualized word representations.
    • Pre-Trained Vision Models, such as:
      • ResNet Models for image recognition.
      • YOLO Models for object detection.
  • Specialized Architecture Models, such as:
    • Trained Neural Network Structures, such as:
      • Trained Bidirectional LSTM for sequence modeling.
      • Trained Transformer Model for attention-based processing.
    • Trained Ensemble Models through stacked ensemble-based learning.
  • ...
• Counter-Example(s):
  • Untrained Models, which lack learned parameters from training processes.
  • Machine Learning Model Familys, which represent model architectures without specific parameters.
  • Rule-Based Systems, which use explicit rules rather than learned patterns.
  • Heuristic Models, which apply predetermined logic without statistical learning.
  • Random Functions, which generate outputs without learned relationships.
• See: Model Training Task, Model Evaluation Task, Model Deployment System, Predictive Model, Machine Learning Algorithm, Training Dataset, Model Performance Metric, Hyperparameter Optimization Task, Transfer Learning, Model Versioning, AI Model Interpretability Measure.

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References

2011

• http://code.google.com/apis/predict/docs/glossary.html
  • When you train the Prediction API against a data set, it creates a model based on that specific training data. All queries for the Prediction API are sent to a model trained against a specific training data.

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