Technologies Used:
Scikit-learn: A machine learning library for Python.
Apache Spark: A unified analytics engine for large-scale data processing.
Jenkins: A continuous integration and continuous delivery (CI/CD) tool.
Object storage: A scalable and durable storage solution for large amounts of data.
PostgreSQL: A relational database management system.
The goal was to improve the accuracy and efficiency of leak detection compared to traditional rule-based methods.
We implemented a machine learning model using scikit-learn and Apache Spark. The model was trained on a historical dataset of water consumption and pressure data. We used various features, including time of day, day of the week, weather conditions, and sensor data to train the model.
To make the model scalable and performant, we deployed it on a Spark cluster. We also used Jenkins for continuous integration and continuous delivery (CI/CD) to automate the model training and deployment process.
For data storage, we used a combination of object storage and PostgreSQL. Object storage was used to store large volumes of historical data, while PostgreSQL was used to store the model parameters and metadata.
The machine learning model was able to detect water leaks with a high degree of accuracy. The model was also able to identify leaks that were not detected by the traditional rule-based methods.
The implementation of the machine learning model resulted in the following benefits for our client:
