ACL Digital Transformed Data Science Pipelines for a Global Rail Transport Leader
Overview
A global leader in rail transport sought to revolutionize its data analytics processes by industrializing its Data Science Pipeline. Operating across passenger transportation, signaling, and locomotive sectors, the company aimed to enhance odometry and radioscopy mobility analytics efficiency to stay competitive in a rapidly evolving industry.
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Challenges
Extracting, managing, and operationalizing large volumes of odometry data
Building robust pipelines to train, deploy, and automate anomaly detection in mobility analytics
Ensuring seamless deployment of machine learning (ML) models and their integration with user interfaces for performance monitoring
Minimizing manual intervention to reduce operational inefficiencies and errors
Solutions
- Extracted code from Jupyter notebooks and created a data pipeline to transfer odometry data to a Minio bucket efficiently.
- Designed a training pipeline for anomaly detection and classification of anomalous events.
- Deployed ML models as OpenFaaS functions and implemented a CI pipeline to automate model deployment.
- Developed and tested a user interface application to display the performance of the deployed models.
Outcomes
Automation
Enabled the automated detection and classification of anomalies, significantly reducing manual workload and errors
Efficiency
This marked a substantial improvement over traditional OCR methods, which struggled with the degraded quality of the images
Scalability
Provided a robust infrastructure for deploying future analytics use cases, enhancing adaptability to evolving business needs
