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---
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license: apache-2.0
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---
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Cookiecutter-MLOps
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==============================
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A cookiecutter template employing MLOps best practices, so you can focus on building machine learning products while
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having MLOps best practices applied.
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Instructions
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------------
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1. Clone the repo.
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2. Run `make dirs` to create the missing parts of the directory structure described below.
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3. *Optional:* Run `make virtualenv` to create a python virtual environment. Skip if using conda or some other env manager.
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1. Run `source env/bin/activate` to activate the virtualenv.
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4. Run `make requirements` to install required python packages.
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5. Put the raw data in `data/raw`.
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6. To save the raw data to the DVC cache, run `dvc add data/raw`
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7. Edit the code files to your heart's desire.
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8. Process your data, train and evaluate your model using `dvc repro` or `make reproduce`
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9. To run the pre-commit hooks, run `make pre-commit-install`
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10. For setting up data validation tests, run `make setup-setup-data-validation`
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11. For **running** the data validation tests, run `make run-data-validation`
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12. When you're happy with the result, commit files (including .dvc files) to git.
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Project Organization
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------------
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├── LICENSE
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├── Makefile <- Makefile with commands like `make dirs` or `make clean`
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├── README.md <- The top-level README for developers using this project.
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├── data
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│ ├── processed <- The final, canonical data sets for modeling.
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│ └── raw <- The original, immutable data dump
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│
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├── models <- Trained and serialized models, model predictions, or model summaries
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│
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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│ the creator's initials, and a short `-` delimited description, e.g.
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│ `1.0-jqp-initial-data-exploration`.
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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│ └── figures <- Generated graphics and figures to be used in reporting
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│ └── metrics.txt <- Relevant metrics after evaluating the model.
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│ └── training_metrics.txt <- Relevant metrics from training the model.
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│
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
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│ generated with `pip freeze > requirements.txt`
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│
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├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
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├── src <- Source code for use in this project.
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│ ├── __init__.py <- Makes src a Python module
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│ │
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│ ├── data <- Scripts to download or generate data
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│ │ ├── great_expectations <- Folder containing data integrity check files
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│ │ ├── make_dataset.py
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│ │ └── data_validation.py <- Script to run data integrity checks
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│ │
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│ ├── models <- Scripts to train models and then use trained models to make
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│ │ │ predictions
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│ │ ├── predict_model.py
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│ │ └── train_model.py
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│ │
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│ └── visualization <- Scripts to create exploratory and results oriented visualizations
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│ └── visualize.py
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│
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├── .pre-commit-config.yaml <- pre-commit hooks file with selected hooks for the projects.
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├── dvc.lock <- constructs the ML pipeline with defined stages.
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└── dvc.yaml <- Traing a model on the processed data.
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--------
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<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
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---
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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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Made with 🐶 by [DAGsHub](https://dagshub.com/).
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