During a model-design iteration cycle, a regulated finance team is running hundreds of hyperparameter trials on developer workstations that have no permanent network connection. The regulations state that years later an auditor must be able to recreate any past experiment-including the exact code revision, training data, parameters, metrics, and model artifacts-using only the project's Git repository and local storage. The team also wants to sort and compare experiment metrics from the command line while they iterate. Which experiment-tracking strategy BEST meets all of these requirements with the least additional infrastructure?
Deploy a central MLflow tracking server with a Postgres backend and S3 artifact store; rely on the server to retrieve code and data for audits.
Record hyperparameters and results in a shared spreadsheet and save trained model files to a timestamped directory on a network drive.
Use DVC experiments so that metrics, parameters, and artifact pointers are version-controlled alongside the code; reproduce runs with dvc exp apply and compare them with dvc exp show.
Run mlflow.autolog() locally and log runs to a SQLite-backed MLflow tracking server, tagging each run manually with the current Git commit.
Data Version Control (DVC) stores every experiment's parameters, metrics, and pointers to data and artifacts as plain text files that live in the same Git commit as the code. Commands such as dvc exp show list and rank experiments in the terminal, and dvc exp apply <exp-id> can reproduce the full workspace-including the original data snapshot-offline from the repository. Because the metadata is versioned with Git, no external database or tracking server is required, satisfying long-term reproducibility and audit needs.
A local SQLite-backed MLflow instance still relies on a separate mlruns directory that must be backed up and manually linked to code revisions. A remote MLflow server introduces even more external components (tracking server, database, artifact store) that violate the "Git-only" constraint. Spreadsheets and file-naming conventions are not reliable or verifiable and break reproducibility guarantees.
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What is DVC and how does it help in tracking experiments?
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How does `dvc exp show` work, and why is it useful?
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Why are external MLflow servers or spreadsheets less reliable for audit requirements?