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.