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Volvo
Revamping Volvo's Data Analytics with Cloud Technology
Volvo is a Swedish multinational automotive company known for its commitment to safety, innovative engineering, and Scandinavian design. They specialize in manufacturing and selling a wide range of vehicles, including sedans, SUVs, crossovers, and electric cars.
Business Challenge
The client needed to deploy a machine learning-based scoring model built in Python for lead prediction. The model required at least six months of historical data per training run. They also needed a push notification system and several supporting applications. Everything had to be cloud-agnostic and deployable on both AWS and Azure.
Key Features
Deployed a Python-based scoring model for predicting leads.
Automated the entire model lifecycle — training, testing, and deployment — using CI/CD pipelines.
Ensured scalable and efficient infrastructure for handling large volumes of data.
Scheduled model updates to run daily with the latest data.
Developed a push notification system and integrated it with Retool.
Results
Optimized the Python scoring model to achieve over 94% accuracy.
Dockerized the application to run on dedicated nodes in a Kubernetes cluster on both AWS and Azure.
Built a data pipeline using Python to preprocess data, clean missing values, and normalize the dataset for model training — automated to run daily and feed the latest data into the retraining process.
Tech Stack
Python
C#
Kubernetes
Azure DMS
Java
Nodejs
Azure