Standard 5 stops to get here

Model Registry

A centralized repository for tracking, versioning, and managing trained models.

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. Model Serving ✓ understood

    Deploying trained models as services that can handle prediction requests in production environments.

  5. MLOps ✓ understood

    Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.

  6. Model Registry · you are here ✓ understood

A centralized repository for tracking, versioning, and managing trained models.

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

  • MLOps
  • Versioning
  • Model Management

Where it sits

Before this

MLOps
Model Registry

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Model Versioning Tracking different versions of models to enable reproducibility and rollback. Shipping AI Model Lineage Tracking the origin and dependencies of models including data, code, and parameters. Shipping AI Experiment Tracking Recording hyperparameters, metrics, and artifacts from training runs for comparison and reproducibility. Shipping AI CI/CD for ML Continuous integration and deployment practices adapted for machine learning pipelines.