Establishing ML governance
When working on ML initiatives and requirements, ML governance must be taken into account as early as possible. Companies and teams with poor governance experience both short-term and long-term issues due to the following reasons:
- The absence of clear and accurate inventory tracking of ML models
- Limitations concerning model explainability and interpretability
- The existence of bias in the training data
- Inconsistencies in the training and inference data distributions
- The absence of automated experiment lineage tracking processes
How do we deal with these issues and challenges? We can solve and manage these issues by establishing ML governance (the right way) and making sure that the following areas are taken into account:
- Lineage tracking and reproducibility
- Model inventory
- Model validation
- ML explainability
- Bias detection
- Model monitoring
- Data analysis and data quality reporting
- Data integrity...