Standard 5 stops to get here · leads to 1

Model Versioning

Tracking different versions of models to enable reproducibility and rollback.

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 Versioning · you are here ✓ understood

Tracking different versions of models to enable reproducibility and rollback.

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

  • MLOps
  • Version Control
  • Model Registry

Where it sits

Before this

MLOps
Model Versioning

Leads to

Model Lineage

Explore nearby

Shipping AI Model Registry A centralized repository for tracking, versioning, and managing trained models. Shipping AI Data Versioning Tracking different versions of datasets to ensure reproducibility and manage changes. Shipping AI Model Lineage Tracking the origin and dependencies of models including data, code, and parameters. Shipping AI Blue-Green Deployment Running two identical environments to enable zero-downtime model updates and rollbacks.