Reference 7 stops to get here

Model Lineage

Tracking the origin and dependencies of models including data, code, and parameters.

Your route here

7 stops · basics first

Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
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.
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.
Model Serving ✓ understood
Deploying trained models as services that can handle prediction requests in production environments.
MLOps ✓ understood
Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.
Model Versioning ✓ understood
Tracking different versions of models to enable reproducibility and rollback.
Data Versioning ✓ understood
Tracking different versions of datasets to ensure reproducibility and manage changes.
Model Lineage · you are here ✓ understood

Tracking the origin and dependencies of models including data, code, and parameters.

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

Before this

Model Lineage

Leads to

Nothing yet: a destination in its own right.