This project is realized during the formation "MLOps" at Datascientest. It aims to develop and test a machine learning model and create an API to access the model and make real time predictions. The raw data is loaded in data/raw (added to gitignore because of the total size) before being processed.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original data dump, taken from data.gouv.fr
│
├── logs <- Logs from the API tests (added to gitignore)
│
├── models <- Trained models and encoders used int eh API to make predictions
│
├── 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`.
│ Used to explore data and test models before creating the API
│
├── 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
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── api <- Scripts create the API and the API Container
│ │ └── app.py
│ │ └── auth.py
│ │ └── log_module.py
│ │ └── Dockerfile_api
│ │ └── api-deployment.yaml
│ │ └── service-api.yaml
│ │
│ ├── dags <- Airflow module
│ │ └── pipeline_dag.py.py
│ │
│ ├── data <- Scripts to download or generate data and create the Data Container
│ │ ├── make_dataset.py
│ │ ├── build_features.py
│ │ ├── config.py
│ │ ├── etl.py
│ │ └── Dockerfile_data
│ │
│ ├── k8s <- Kubernetes files
│ │ ├── api-deployment.yaml
│ │ ├── data-deployment.yaml
│ │ ├── models-deployment.yaml
│ │ ├── persistent-volume-claim.yaml
│ │ ├── persistent-volume.yaml
│ │ ├── service-api.yaml
│ │ ├── service-data.yaml
│ │ └── service-models.yaml
│ │
│ ├── models <- Scripts to train models and create the Model Container
│ │ ├── predict_model.py
│ │ ├── train_model.py
│ │ ├── config.py
│ │ ├── model_pipeline.py
│ │ └── Dockerfile
Project based on the cookiecutter data science project template. #cookiecutterdatascience