This is a toy project to demonstrate the use of MLOps practices in ML projects used in the tutorial Software Engineering for ML Systems at the EibAIS 2025 school.
├── LICENSE <- Open-source license if one is chosen
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- Guides to implement some of the MLOps practices
│
├── 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`.
│
├── pyproject.toml <- Project configuration file with package metadata for
│ src and configuration for tools like black
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
└── src <- Source code for use in this project.
│
├── config.py <- Store useful variables and configuration
│
├── data <- Scripts to process data
│ ├── download_raw_dataset.py <- Download raw dataset
│ ├── preprocess.py <- Preprocess raw dataset
│ ├── split_data.py <- Split raw dataset into train, validation, and test sets
│ ├── gx_context_configuration.py <- Great Expectations context configuration
│ └── validate_data.py <- Validate quality of the data with Great Expectations
│
└── modeling
├── __init__.py
├── evaluate_model.py <- Code to evaluate the latest trained model
└── train.py <- Code to train models