Cookiecutter-MLOps

A cookiecutter template employing MLOps best practices, so you can focus on building machine learning products while having MLOps best practices applied.

Instructions

1. Clone the repo.
2. Run make dirs to create the missing parts of the directory structure described below.
3. Optional: Run make virtualenv to create a python virtual environment. Skip if using conda or some other env manager.
   1. Run source env/bin/activate to activate the virtualenv.
4. Run make requirements to install required python packages.
5. Put the raw data in data/raw.
6. To save the raw data to the DVC cache, run dvc add data/raw
7. Edit the code files to your heart's desire.
8. Process your data, train and evaluate your model using dvc repro or make reproduce
9. To run the pre-commit hooks, run make pre-commit-install
10. For setting up data validation tests, run make setup-setup-data-validation
11. For running the data validation tests, run make run-data-validation
12. When you're happy with the result, commit files (including .dvc files) to git.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│   └── metrics.txt    <- Relevant metrics after evaluating the model.
│   └── training_metrics.txt    <- Relevant metrics from training the model.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   ├── great_expectations  <- Folder containing data integrity check files
│   │   ├── make_dataset.py
│   │   └── data_validation.py  <- Script to run data integrity checks
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
├── .pre-commit-config.yaml  <- pre-commit hooks file with selected hooks for the projects.
├── dvc.lock           <- constructs the ML pipeline with defined stages.
└── dvc.yaml           <- Traing a model on the processed data.

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Project based on the cookiecutter data science project template. #cookiecutterdatascience

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To create a project like this, just go to https://dagshub.com/repo/create and select the Cookiecutter DVC project template.

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