Tracking different versions of datasets to ensure reproducibility and manage changes.
This concept is essential for understanding practical deployment and forms a key part of modern AI systems.
Related Concepts
- MLOps
- Reproducibility
- Data Management
Standard 5 stops to get here · leads to 1
Data Versioning
Tracking different versions of datasets to ensure reproducibility and manage changes.
Your route here
5 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.
- Data Versioning · you are here ✓ understood
Where it sits
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Shipping AI Model Versioning Tracking different versions of models to enable reproducibility and rollback. Shipping AI Model Reproducibility The ability to recreate exact model results given the same code, data, and environment. Shipping AI Model Lineage Tracking the origin and dependencies of models including data, code, and parameters. Shipping AI Feature Store A centralized platform for managing, storing, and serving features for ML models.