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dssi-py_new's Introduction

Data Product Development and Deployment with Streamlit

Requirements

  1. Setup Github account
  2. Install required packages:
pip install -r requirements.txt

Prelude: Try Streamlit

  1. Create toy application with Streamlit.
  2. Push repository to GitHub.
  3. Deploy on Streamlit community cloud.

Sample application code: toy-app.py

Step 1: Train and Save Model

  1. Perform EDA and model development on Jupyter notebook.
  2. Develop a training script to automate model training and persistance.
  3. Run the training script to train a loan approval model:
python src/training.py --data_path data/loan_dataset.csv --model_path models/ --f1_criteria 0.6

Sample model training notebook: DSSI_LoanModel.ipynb
Sample training script: training.py

Step 2: Create App and Load Model

  1. Develop an inference script to serve predictions.
  2. Create a loan approval application with Streamlit that automates decisions with user inputs and trained model.

Sample application code: app.py
Sample inference script: inference.py

Step 3: Test App Locally

Run and test the application locally:

streamlit run app.py

Step 4: Deploy App Online

  1. Commit repository to GitHub.
  2. Deploy on Streamlit community cloud.# dssi-py_new

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