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---
license: apache-2.0
---
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.
--------
<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>
---
To create a project like this, just go to https://dagshub.com/repo/create and select the **Cookiecutter DVC** project template.
Made with 🐶 by [DAGsHub](https://dagshub.com/).
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