moj-analytical-services / splink_demos

Overview

This documentation provides a step-by-step guide on scaling the moj-analytical-services/splink_demos project for production environments. It addresses necessary preparations and considerations for handling large datasets efficiently.

Prerequisites

Ensure the following software is installed and properly configured:

  • DuckDB: Essential for ingesting and processing datasets that may include over a million records, which is aligned with the project’s architecture.

  • Python: Recommended version that supports Jupyter Notebook extensions and data manipulation libraries.

Step-by-Step Guide to Production Scaling

1. Prepare the Data

Before scaling, make sure your datasets are clean and pre-processed. Example datasets include:

# Example of a dataset from febrl
rec-3778-org,sascha,demarco,3,franks place,tantallon,gracetown,5169,tas,19960906,9430667
rec-3714-org,tayah,pascoe,211,o'halloran circuit,stonehenge,salisbury east,2285,nsw,19410809,9400248

This dataset can be stored in data/febrl/dataset4a.csv. Ensure all data adheres to the expected formats and values to avoid processing errors during scaling.

2. Choose the Right Backend

Utilizing DuckDB is essential when working with datasets nearing the size of one million records. The following initialization code snippet sets up the DuckDB backend for usage within the project scope:

import duckdb

# Connect to DuckDB
conn = duckdb.connect(database=':memory:', read_only=False)

# Load dataset into DuckDB
data = conn.execute("SELECT * FROM read_csv_auto('data/febrl/dataset4a.csv')").fetchdf()

3. Model Scaling Strategy

When scaling the project, employ appropriate strategies for model parameter estimation and prediction. Use the Jupyter Notebook tutorial 07_Quality_assurance.ipynb as a guide for assessing model accuracy.

Example code for calculating evaluation metrics:

metrics = {
    "N_rate": 0,
    "tp_rate": -26.441763,
    "tn_rate": 1.097075e-08,
    "fp_rate": 1225.0,
    "fn_rate": 80.0,
    "precision": (1145.0 / (1145.0 + 1225.0)),
    "recall": (1145.0 / (1145.0 + 80.0)),
    "f1": 2 * (precision * recall) / (precision + recall)
}

4. Utilize Efficient Data Structures

Implement efficient data handling structures, especially for real-time predictions. Using Python’s in-built libraries like Pandas can enhance the speed and efficiency when manipulating data.

Example code snippet to load and manipulate data:

import pandas as pd

# Load a CSV file into a Pandas DataFrame
df = pd.read_csv('data/febrl/dataset4a.csv')

# Example manipulation: filtering out rows with NaN values
filtered_df = df.dropna()

5. Optimize the Deployment Pipeline

Make sure to set up a continuous integration (CI) pipeline that reflects changes in your code and automates testing and deployment for various environments. Using tools like GitHub Actions or Travis CI can streamline this process.

6. Monitor and Adjust

After deploying, it’s critical to monitor system performance. Metrics from the processing phase must be captured to adjust the underlying infrastructure as necessary to ensure quality and efficiency.

For instance, establish monitoring scripts to gauge resource utilization:

#!/bin/bash
# Monitor CPU and memory usage
top -b -n1 >> resource_usage.log

7. Documentation and User Feedback

Post-deployment, maintain a robust documentation strategy that outlines not just deployment processes but also user feedback mechanisms to continuously refine the project.

Conclusion

Scaling production systems for moj-analytical-services/splink_demos requires thoughtful integration of data handling and model management practices. By following the outlined steps, practitioners can ensure that their deployments are both efficient and maintainable.

For further reference and detailed metrics evaluation, check out notebooks and examples available in the dedicated repository splink_demos.