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mlops's Introduction

Churn Model..

MLOps Toy Example End to End ! ๐Ÿ”ฅ

You might find below links useful

  1. Connect VSCode to Git
  2. MLFlow Port
  3. Heroku App
  4. Streamlit App
  5. Dataset

Project Organization

โ”œโ”€โ”€ LICENSE
โ”œโ”€โ”€ Makefile           <- Makefile with commands like `make data` or `make train`
โ”œโ”€โ”€ README.md          <- The top-level README for developers using this project.
โ”œโ”€โ”€ data
โ”‚ย ย  โ”œโ”€โ”€ external       <- Data from third party sources.
โ”‚ย ย  โ”œโ”€โ”€ interim        <- Intermediate data that has been transformed.
โ”‚ย ย  โ”œโ”€โ”€ processed      <- The final, canonical data sets for modeling.
โ”‚ย ย  โ””โ”€โ”€ raw            <- The original, immutable data dump.
โ”‚
โ”œโ”€โ”€ docs               <- A default Sphinx project; see sphinx-doc.org for details
โ”‚
โ”œโ”€โ”€ models             <- Trained and serialized models, model predictions, or model summaries
โ”‚
โ”œโ”€โ”€ notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
โ”‚                         the creator's initials, and a short `-` delimited description, e.g.
โ”‚                         `1.0-jqp-initial-data-exploration`.
โ”‚
โ”œโ”€โ”€ references         <- Data dictionaries, manuals, and all other explanatory materials.
โ”‚
โ”œโ”€โ”€ reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
โ”‚ย ย  โ””โ”€โ”€ figures        <- Generated graphics and figures to be used in reporting
โ”‚
โ”œโ”€โ”€ requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
โ”‚                         generated with `pip freeze > requirements.txt`
โ”‚
โ”œโ”€โ”€ setup.py           <- makes project pip installable (pip install -e .) so src can be imported
โ”œโ”€โ”€ src                <- Source code for use in this project.
โ”‚ย ย  โ”œโ”€โ”€ __init__.py    <- Makes src a Python module
โ”‚   โ”‚
โ”‚ย ย  โ”œโ”€โ”€ data           <- Scripts to download or generate data
โ”‚ย ย  โ”‚ย ย  โ””โ”€โ”€ make_dataset.py
โ”‚   โ”‚
โ”‚ย ย  โ”œโ”€โ”€ features       <- Scripts to turn raw data into features for modeling
โ”‚ย ย  โ”‚ย ย  โ””โ”€โ”€ build_features.py
โ”‚   โ”‚
โ”‚ย ย  โ”œโ”€โ”€ models         <- Scripts to train models and then use trained models to make
โ”‚   โ”‚   โ”‚                 predictions
โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ predict_model.py
โ”‚ย ย  โ”‚ย ย  โ””โ”€โ”€ train_model.py
โ”‚   โ”‚
โ”‚ย ย  โ””โ”€โ”€ visualization  <- Scripts to create exploratory and results oriented visualizations
โ”‚ย ย      โ””โ”€โ”€ visualize.py
โ”‚
โ””โ”€โ”€ tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Project based on the cookiecutter data science project template. #cookiecutterdatascience

The original scaffold is taken from a great article by Shanaka. A great post by him summarizing Krish Naik's youtube channel.

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