The Challenge
Sonova was aiming to mature and scale its machine learning function but faced critical bottlenecks across engineering productivity, model deployment, and operational reliability. While their data science team had developed early models (such as one for customer churn), they lacked automation, consistent infrastructure, and standardized processes to productionalize these efforts.
The organization needed a replicable ML blueprint to streamline deployments, reduce engineering workload, and ensure that models could reliably scale across future use cases. Their 5-person data science team was overstretched, with much of their time spent on manual tasks like generating inference scores and retraining models—limiting speed to market and creating organizational risk around uptime, accuracy, and knowledge silos.
The Solution
Tribe partnered with Sonova to build a mature and scalable ML infrastructure. Over 12 weeks, the team delivered a framework to automate the deployment, monitoring, and continuous delivery of ML models—starting with their churn prediction model.
This included automated pipelines for training, inference, evaluation, and reporting, all designed to operate on a scheduled basis or in response to production code changes. Tribe also introduced code testing frameworks, team-wide documentation practices, and real-time metrics that help detect model drift or underperformance.
In parallel, the collaboration focused on roadmap planning and architecture reviews, enabling Sonova to expand the system for future models while reducing time to market and technical risk.
Key Features
- Automated ML Pipelines: Scheduled workflows for training, evaluation, inference score generation, and business metric tracking—saving ~32 hours/month per model.
- Testing Frameworks & CI/CD: Automated test runners and unit testing pipelines catch bugs before models hit production.
- MLOps Standardization: Codified Sonova’s ML lifecycle into a repeatable set of best practices.
- Knowledge Sharing: Team-wide documentation and training to remove dependencies on individual contributors.
- Time-to-Market Acceleration: Fully operationalized churn prediction model within 12 weeks, enabling proactive interventions.
How It Works
Tribe built an end-to-end automation layer on top of Sonova’s ML workflows using tools like Azure Machine Learning Studio and CI/CD pipelines. Here’s how it functions:
1. Scheduled Training and Inference
ML pipelines automatically retrain and generate inference scores based on calendar schedules or when code is updated.
2. Model Evaluation & Business Metrics
Frameworks monitor model accuracy and business KPIs regularly—providing early indicators of drift or performance issues.
3. Test & Validation Automation
Code testing infrastructure ensures that breaking changes are caught before deployment.
4. Team Enablement
Standardized documentation, knowledge transfer, and process maturity reduce single points of failure.
This system increased engineering capacity by automating up to 32 hours/month per model, across key manual workflows like model training, evaluation, and reporting.
Impact & The Future
In just 12 weeks, Sonova increased the capacity of its data science team by 20%, reduced engineering overhead, and improved production reliability. By automating manual workflows, the team gained back time and confidence—accelerating the delivery of new ML initiatives.
Notable outcomes include:
- 20% reduction in engineering spend
- Faster time to market for models like churn prediction
- Elimination of project silos, enabling vacation coverage and cross-team continuity
- Reduced risk related to uptime, accuracy, and security
With foundational infrastructure in place, Sonova is now positioned to scale ML across the enterprise, leveraging automation to power new use cases with minimal incremental effort. This project served as both a catalyst and a blueprint for future AI adoption at scale.
