SonovaThe customer this was delivered for.Saved ~32 Hours/Month per Model and Increased Data Science Capacity 20%
Key results
- 5-person data science team
- 12 weeks to deliver an MLOps framework and operationalize the churn prediction model
- ~32 hours/month saved per model via automated ML pipelines
Sonova wanted to mature and scale its machine learning function but faced bottlenecks in engineering productivity, model deployment, and operational reliability. Early models (including customer churn) existed, but automation, consistent infrastructure, and standardized production processes were missing. The 5-person data science team was overstretched and spent significant time on manual inference scoring and retraining. This slowed speed to market and increased risk around uptime, accuracy, and knowledge silos. To address this, a scalable ML infrastructure and repeatable blueprint were implemented to streamline deployments and reduce manual effort. Over 12 weeks, an MLOps framework was delivered to automate deployment, monitoring, and continuous delivery starting with the churn prediction model. Automated pipelines covered training, inference, evaluation, and reporting on schedules or triggered by production code changes. Testing/CI-CD practices, documentation, and real-time metrics for drift and underperformance detection were also put in place to support ongoing expansion. As a result, manual workflows were automated by up to ~32 hours/month per model, increasing engineering capacity. In 12 weeks, Sonova increased data science team capacity by 20% and reduced engineering overhead while improving production reliability. The effort also delivered a 20% reduction in engineering spend and fully operationalized the churn prediction model within the project timeline. Standardized practices reduced silos and improved continuity through shared documentation and enablement.
Tribe AI