ML governance applies many of the same data governance practices to ML. Data quality and data integration need to provide the data required for model training and production deployment (feature stores are one important aspect of this). Responsible artificial intelligence (AI) is paying special attention to using sensitive data for building models. Additional ML governance capabilities include enabling people to participate in model building, deployment, and monitoring; documenting model training, versioning, supported use cases, and guiding ethical model use; and monitoring the model in production for accuracy, drift, overfitting, and underfitting.
Generative AI requires additional data governance capabilities, like quality and integrity of data to support adaptation of foundation models for training and for inference, governance of Generative AI toxicity and bias, and foundation model (FM) operations: FMOps.
You can support AI/ML with the same data governance program. Data preparation is necessary to transform data into a form that AI/ML models can use for training and production inference—but the most efficient data preparation is the preparation you don’t have to do. Data scientists spend too much time preparing data for each use case—your data governance team can help alleviate this undifferentiated heavy lifting. In addition, data governance can oversee the creation of shaped feature stores to be used across AI and ML use cases.
Finally, sensitive data needs to be protected appropriately, so your team can mitigate the risks of sensitive data being used to train the foundation models.
Much like analytics in general, you have to govern the use of AI/ML models that you build or customize. Ideally, this should be closely associated with analytics governance, because that function will know how to support various business areas.