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Volvo
Revamping Volvo's Data Analytics with Cloud Technology
Scoring model accuracy
94%+
Retraining
manual
→
daily, automated
Clouds served
AWS + Azure
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.
What changed
Before
Data
Manual preparation
Model
Python scoring
run by hand
Serving
Single environment
After
Pipeline
Python ETL
clean · normalise · daily
Model
Scoring model
94%+ accuracy
Auto-retrain
Package
Docker
Serving
Kubernetes
AWS + Azure
A hand-run scoring model became a daily pipeline whose output serves from Kubernetes on two clouds.
What we built
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.
Stack
Engagement: 2 years · Real-time & Industrial Data Platforms, Product Engineering, Platform & Cloud Engineering