my_mlops_project

TODO

Project structure

The directory structure of the project looks like this:

├── .github/                  # Github actions and dependabot
│   ├── dependabot.yaml
│   └── workflows/
│       └── tests.yaml
├── configs/                  # Configuration files
├── data/                     # Data directory
│   ├── processed
│   └── raw
├── dockerfiles/              # Dockerfiles
│   ├── api.Dockerfile
│   └── train.Dockerfile
├── docs/                     # Documentation
│   ├── mkdocs.yml
│   └── source/
│       └── index.md
├── models/                   # Trained models
├── notebooks/                # Jupyter notebooks
├── reports/                  # Reports
│   └── figures/
├── src/                      # Source code
│   ├── project_name/
│   │   ├── __init__.py
│   │   ├── api.py
│   │   ├── data.py
│   │   ├── evaluate.py
│   │   ├── models.py
│   │   ├── train.py
│   │   └── visualize.py
└── tests/                    # Tests
│   ├── __init__.py
│   ├── test_api.py
│   ├── test_data.py
│   └── test_model.py
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE
├── pyproject.toml            # Python project file
├── README.md                 # Project README
├── requirements.txt          # Project requirements
├── requirements_dev.txt      # Development requirements
└── tasks.py                  # Project tasks

Available Tasks

The following tasks can be executed using the invoke command:

• create_environment: Create a new conda environment for the project.
• requirements: Install project requirements.
• dev_requirements: Install development requirements.
• preprocess_data: Preprocess raw data and save it to the processed directory.
• train: Train the model.
• evaluate: Evaluate the trained model.
• visualize: Visualize model predictions.
• test: Run tests and generate a coverage report.
• docker_build: Build Docker images for training and API.
• build_docs: Build the project documentation.
• serve_docs: Serve the project documentation locally.

To run a task, use the following command:

invoke <task_name>

For example, to train the model, use:

invoke train

Created using mlops_template, a cookiecutter template for getting started with Machine Learning Operations (MLOps).