Deployment
Once the Data Science Management (DSM) process has been completed and the results have been validated, the solution can be deployed to production.
A successful deployment should provide a repeatable, reliable, and maintainable environment for ongoing data loading, quality control, analytics, and prediction workflows.
Production Deployment
Production deployments should include:
- Automated data loading and processing
- Data quality monitoring and validation
- Model execution and prediction workflows
- Result management and traceability
- Monitoring and operational support
Database Manager
One option for deploying and managing DSM projects is our open source Database Manager platform. Database Manager provides integrated support for PPDM databases, DataOps pipelines, data quality workflows, and predictive analytics.
Database Manager can be deployed either on-premises or in the cloud, providing a flexible platform for managing production DSM environments. Additional information is available on the Products page.