Enterprise scenarios that highlight the importance of MLOps governance
To understand the importance of MLOps governance, let’s go through some real-world scenarios that highlight this.
Scenario 1 – limiting bias in AI solutions
Consider a financial services firm deploying a suite of ML models to predict credit risk. A large firm in the finance sector would have an array of internal policies around the data access, usage, and risk assessment of predictive models that its ML solutions will need to adhere to. This could range from limits on what data can be used for such purposes to who can access the model’s outputs. It would also be obligated to follow several regulatory requirements, such as preventing bias against protected classes in its decision-making models. For example, a bank would need to ensure that its decision-making process around loan approval is not biased based on race or gender. Even if the regulators can’t decipher the underlying ML...
