ML Governance is an internal framework to control the processes for ML development, implement and track activities, and assign roles.
What is ML Governance?
ML Governance is an internal framework of enterprises that controls the processes for model development, implements and tracks activity for ML models, validation and usage, and assignment of roles and responsibilities.
ML governance helps mitigate risk for model failure and ensures regulatory compliance for ML projects. Proper ML governance brings better control and visibility into how models perform in production. Seamless integration of ML practices with organizational IT policies helps reduce project risks and improve operational efficiencies.
ML governance allows model audits for possible performance issues and anomalies in production. Such audits help indicate unforeseen model quality issues, skew, drift, and help models operate smoothly in the production environment.
ML governance also helps track ML projects for various changes such as model ownership, the team involved, and relevant organizational changes.
ML Governance Challenges
Manual processes and incomplete solutions
Current solutions used to implement ML governance involve manual workflows and disparate tools that fail to serve as an integrated and complete suite for ML governance.
Incomplete knowledge about practices and regulations
Since model governance is evolving and has not matured yet, many enterprises struggle to finalize a clear roadmap on its enterprise-specific implementation. A changing regulatory environment poses new challenges to meet compliance requirements.
ML and IT policy integration challenges
Integrating ML governance practices with broader IT policies would drive better results. But as ML is considered more of a fancy initiative by many organizations, it becomes difficult to integrate well-streamlined IT policies and ML practices.
Risk to brand and company assets
Poorly governed ML projects impose risk to brand and company assets. Models that are not monitored properly can underperform. Models that drift can lose customer trust in a brand.
Addressing ML Governance Challenges
ML governance requires best practices, automation, and attention to the following key areas:
- Tools to enhance model operations in production and efficient deployments
- Tools to monitor model accuracy, model decay, drifts, and data consistency issues
- Model documentation with sources of model data used for training, model metadata generated in the ML lifecycle
- Details such as validation and approval required at each stage - data quality checks, best practices for validation datasets, compliance testing, and code quality
- Model compliance with regulatory requirements – any broader ethical standards to be considered, monitor prod model output for compliance requirement.
- Records of model ownership details - teams involved, information on role change
ML governance is challenged by manual processes and a lack of unified tools. However, a complete monitoring solution such as Censius AI Observability Platform helps you attain the required automation for the ML monitoring process. The platform monitors models, data (and/or logs) for accuracy, drifts, and data quality issues. It alerts users on possible model quality issues and tracks different performance metrics for effective ML governance.