ML Project MLOps Pipeline Structure

Full MLOps setup with DVC, MLflow, and CI/CD integration. For production ML systems requiring reproducibility and automation.

#ml #python #mlops #dvc #mlflow #cicd

PNG PDF

Project Directory

ml-pipeline/

src/

ml_pipeline/

__init__.py

data/

make_dataset.py

validate.py

Great Expectations

features/

build_features.py

models/

train.py

predict.py

evaluate.py

pipelines/

DVC pipeline definitions

dvc.yaml

Pipeline DAG

params.yaml

Hyperparameters

data/

DVC-tracked

raw/.gitkeep

processed/.gitkeep

.gitignore

/*.csv, /*.parquet

models/

DVC-tracked artifacts

.gitignore

configs/

train.yaml

Hydra config

model/

data/

tests/

test_data.py

test_model.py

pyproject.toml

dvc.yaml

Root pipeline config

dvc.lock

Pipeline state

mlflow.yaml

MLflow tracking config

Makefile

Common commands

.dvc/

DVC internals

README.md

Why This Structure?

Built for reproducible ML. DVC versions data and models alongside code. MLflow tracks experiments and registers models. The dvc.yaml pipeline ensures dvc repro rebuilds only changed stages. Configs via Hydra allow easy hyperparameter sweeps.

Key Directories

pipelines/-dvc.yaml defines stage dependencies
configs/-Hydra configs for experiment variations
data/-DVC-tracked, actual files in remote storage
models/-Trained models tracked by DVC

Getting Started

pip install -e '.[dev]' and dvc init
dvc remote add -d storage s3://bucket/path
dvc pull (fetch data from remote)
dvc repro (run full pipeline)
mlflow ui (view experiments at localhost:5000)

When To Use This

Production ML systems
Teams needing reproducible experiments
Projects with large datasets
Automated retraining pipelines
Model versioning and A/B testing

Trade-offs

Significant setup-DVC remote, MLflow server required
Learning curve-DVC pipelines, Hydra, MLflow concepts
Overkill for small projects-Use notebook-first instead

Best Practices

Tag releases with dvc metrics diff outputs
Use params.yaml for all hyperparameters
Run dvc repro in CI before merge
Store data checksums, not data, in git