The CPMAI Model Management Framework helps teams master AI operational skills from evaluation to deployment:
Task 1: Evaluating Model Performance and Accuracy
- Implement quality assurance processes for AI models
- Apply model validation techniques using appropriate validation data
- Implement strategies to address overfitting and underfitting
- Align model performance with business key performance indicators
- Assess models against technical KPIs and requirements
- Implement iterative improvement based on evaluation findings
Task 2: Deploying Models for Production Environments
- Transition AI models from training to inference phases
- Implement operationalization strategies for production deployment
- Configure on-premise deployments for sensitive or high-performance needs
- Leverage cloud platforms for scalable AI deployment
- Select appropriate cloud-based machine learning services
- Manage data lifecycles throughout the production environment
- Create procedures for model updates and version control