Image Classification with cifar10 dataset for MLOps course.
The dataset card is available under this link: Dataset Card
The model card is available under this link: Model Card
The cloud provider chosen by the team is Amazon Web Services.
├── LICENSE
├── Dockerfile <- The Dockerfile provides instructions for building a Docker image of the project's runtime environment.
├── data.dvc <- Part of the Data Version Control (DVC) system. It serves as a pointer to a specific version of the project's data stored externally.
├── params.yaml <- Contains configuration parameters and settings for the project.
├── 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 <- Contains the model card and dataset card.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks for testing.
│
├── 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
│ └── make_dataset.py
│
├── features <- Scripts to turn raw data into features for modeling
│ └── build_features.py
│
├── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience