What is Data Platform & MLOps?
Data Platform & MLOps refers to the combination of centralized data infrastructure and operational practices to deploy, monitor, and maintain machine learning models. In Switzerland, it is crucial to ensure scalability, security, and governance while delivering measurable business value.Key Objectives
- Enable reliable and reproducible ML workflows
- Centralize data and ensure high-quality datasets
- Monitor model performance and drift
- Control costs and optimize infrastructure
Data Pipelines & Architecture
Efficient pipelines are the backbone of production-ready ML systems.- Ingest data from multiple sources (structured/unstructured)
- Automate preprocessing, feature engineering, and ETL tasks
- Implement scalable storage and compute architecture (cloud/on-prem)
- Version data and ML models for reproducibility
Monitoring & Observability
Continuous monitoring ensures reliability and early detection of issues.- Track data quality, model accuracy, and prediction drift
- Set up alerts for anomalies and failures
- Maintain logging for auditing and troubleshooting
- Measure KPIs for business impact and operational efficiency
Security & Compliance
Data protection and governance are critical in Switzerland.- Implement access control and role-based permissions
- Encrypt data in transit and at rest
- Follow DSG/DSGVO and industry-specific regulations
- Perform periodic security audits and risk assessments
Cost Management
Optimizing costs is essential for scalable MLOps.- Monitor cloud compute and storage usage
- Use autoscaling and resource scheduling
- Evaluate cost per model deployment and prediction
- Implement budgeting and cost alerts for teams
Swiss Use Cases
- Banking: Real-time fraud detection using ML pipelines
- Manufacturing: Predictive maintenance with centralized data and MLOps
- Healthcare: Patient risk scoring with secure and auditable models
- Retail: Demand forecasting and recommendation engines
FAQ – Frequently Asked Questions
What is the difference between a PoC and production MLOps?
PoC demonstrates feasibility; production MLOps ensures scalability, reliability, monitoring, and governance.How do we monitor models in production?
By tracking data quality, prediction drift, model accuracy, and operational KPIs continuously.Who is responsible for MLOps?
Roles include Data Engineers, ML Engineers, DevOps, and Compliance Leads to ensure operational efficiency and security.Can MLOps be implemented on-premise?
Yes, with careful architecture, version control, and monitoring, on-premise solutions are possible while maintaining compliance.Next Steps
- Design a scalable data platform and define MLOps processes.
- Implement pipelines, versioning, and monitoring.
- Ensure security, compliance, and cost optimization.
- Scale models from PoC to production and measure business impact.
Following these steps ensures Swiss organizations can leverage Data Platforms and MLOps for efficient, secure, and scalable AI operations.