Standard 5 stops to get here

Model Retraining

Periodically updating models with new data to maintain performance as distributions change.

Your route here

5 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Inference ✓ understood

    Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.

  4. Data Drift ✓ understood

    Changes in input data distribution over time that can degrade model performance in production.

  5. Model Drift ✓ understood

    Degradation of model performance over time due to changes in the relationship between features and target.

  6. Model Retraining · you are here ✓ understood

Periodically updating models with new data to maintain performance as distributions change.

This concept is essential for understanding practical deployment and forms a key part of modern AI systems.

  • MLOps
  • Model Drift
  • Continuous Learning

Where it sits

Before this

Model Drift
Model Retraining

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Model Monitoring Tracking model performance, data distribution, and predictions in production to detect issues and degradation. Training Continual Learning Learning new tasks sequentially without forgetting previously learned tasks, addressing catastrophic forgetting. Shipping AI Model Performance Degradation Decline in model quality over time due to distribution shift or changing patterns. Shipping AI CI/CD for ML Continuous integration and deployment practices adapted for machine learning pipelines.