|-- Project Organization
| | <- Cookiecutter `https://cookiecutter.readthedocs.io/en/stable/`
|-- Data Version Control
| | <- DVC `https://dvc.org/`
|-- CI+CD - Github
|-- Model Productionization/Deployment
| |-- UI <- Flask
| |-- Scalability and high availability <- k8
|-- FeedBack and Retrain
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── 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 <- A default Sphinx project; see sphinx-doc.org for details
│
├── 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
│
├── 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 visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience
1 . What to do if code base is more than 500mb?
2 . Deploy uing ci-cd to aws/azure/gcp instance.
-------------Deploy in GCP------------
1. Login to github.
2. Go to marketplace.
3. Search for google cloud build.
4. Authorize it
5. Open gcp console
6. search for cloud build >> trigger
7. gcloud projects add-iam-policy-binding hft-demo-v1 --member=serviceAccount:[email protected] --role=roles/container.developer
8. k8s deploy
9. k8s failing due to permission issue and time out
10. Configure terraform for k8s creation: https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/guides/getting-started
Ml flow execute--
mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifact --host 0.0.0.0 -p 123