Module 10
Data Versioning & ML Reproducibility
Make datasets, features, experiments, and model outputs reproducible across time, teams, and environments.
7 lessons · 6 videos · 1h 49m- 10.01
The Reproducibility Problem in ML
Identify the data, code, environment, configuration, randomness, and infrastructure variables required to reproduce an ML run.
- 10.02
Dataset Versioning Principles
Define a versioning strategy for raw data, transformed data, labels, features, and training splits.
- 10.03
DVC for Data and Pipelines
Track a dataset and preprocessing pipeline with DVC and reproduce the same training dataset from versioned artifacts.
- 10.04
LakeFS for Data Lake Branches
Use a LakeFS-style branching workflow to isolate, validate, and merge changes to data lake tables.
- 10.05
Experiment Tracking Metadata
Log parameters, metrics, artifacts, dataset versions, model versions, and environment metadata for an ML experiment.
- 10.06
Training and Serving Data Lineage
Trace a model prediction back to the data sources, feature definitions, transformations, and model version that produced it.
- 10.07
Reproducible Training Splits
Create deterministic train, validation, and test splits that remain stable as new data arrives.