moj-analytical-services / splink_demos

This document provides a comprehensive guide on how to deploy the moj-analytical-services/splink_demos project into a production environment. The deployment process involves several critical steps that should be followed methodically to ensure a successful deployment.

Prerequisites

Before initiating the deployment, ensure that the following prerequisites are met:

  • Python 3.x installed.
  • Required Python packages are defined in requirements.txt.
  • Jupyter environment, if required for interactive data analysis.

Step 1: Set Up the Environment

The first step in the deployment process is to set up a virtual environment and install the necessary dependencies. Use the following commands:

# Create a virtual environment
python3 -m venv splink_demos_env

# Activate the virtual environment
source splink_demos_env/bin/activate  # On Unix or MacOS
# .\splink_demos_env\Scripts\activate  # On Windows

# Install necessary packages
pip3 install -r requirements.txt

Step 2: Configure Jupyter Kernel (Optional)

If Jupyter Notebook is being used for running tutorials or testing, configure the virtual environment to be used as a Jupyter kernel:

python -m ipykernel install --user --name=splink_demos

After setting up the kernel, you can launch Jupyter Lab:

jupyter lab

This allows you to interact with notebooks directly in your browser, providing an interface to engage with the project more dynamically.

Step 3: Data Set Up

Prepare the datasets required by the application. Ensure the following datasets are available:

These datasets serve as test inputs for running the techniques demonstrated in the notebooks. The data files should be placed in the appropriate directories under the project’s structure.

Step 4: Running Quality Assurance Tests

Quality assurance is vital to ensure the model’s performance before deployment. The 07_Quality_assurance.ipynb Jupyter notebook contains crucial metrics and visualizations for assessing the model’s accuracy. Execute the notebook using the configured Jupyter Lab. Key metrics to focus on include:

# Example metrics obtained from the quality assurance notebook
quality_metrics = {
    "N_rate": 0.200873,
    "tp_rate": 0.799127,
    "precision": 0.934694,
    "recall": 1.0,
    "f1": 0.0,
}

These metrics will serve as a benchmark for the performance of the model in production.

Step 5: Deploying the Application

Now that the setup is complete and the quality assurance tests are satisfactory, you can deploy the application. Deploy the application by following these steps:

  1. Set Up Web Server: Ensure that a web server (e.g., Nginx, Apache) is configured and capable of serving the web application. This may include setting reverse proxies if needed.

  2. Containerization (Optional): Consider containerizing the application using Docker to ensure consistency between development and production environments. Refer to Dockerfile and create a container image.

FROM python:3.x
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "your_application.py"]

  1. Launch the Server: Start the application server using your designated framework’s instructions (Flask, Django, etc.).

  2. Verify Deployment: Test the live application to ensure everything is functioning as expected.

Conclusion

Successful deployment of the moj-analytical-services/splink_demos project involves careful setup of the environment, preparing datasets, running quality assurance checks, and finally deploying the application. Following these steps ensures that the application performs optimally in a production setting.

References