Model Governance allows organizations to govern their machine learning securely

Model governance indicates the overall framework of how an organization control its model development and deployment workflow, including rules, protocols, and controls for machine learning models during production — for example, access control, testing, validation, and the tracing of model results.

Although machine learning projects impact organisations, they don't always arrive at their full potential due to inefficiencies and mismanagement in the process. Machine governance is a priority for organisations to get the highest possible return on its machine learning investment.

Definition of Model Governance

Model governance indicates the overall framework of how an organization control its model development and deployment workflow, including rules, protocols, and controls for machine learning models during production — for example, access control, testing, validation, and the tracing of model results. Tracking the model outcomes permits biases to be detected and rectified. It is important for models, which are programmed to learn as they may accidentally become biased that could bring out inaccurate or unethical results.

It is crucial for risk involved models to manage financial portfolios. As these models can impact on an individual or organization's finances directly, it is essential to verify and correct any biases or incorrect learning within the model.

Importance of Model Governance

As machine learning is a relatively new discipline, there are still a lot of inefficiencies that require to be advocated in ML processes. Machine learning projects can be missing essential value without model governance in place.

Clearing risk of model governance is vital to ensure that models involved with finances stay out of dangerous hazards. These models are programmed to continue learning along the run. However, these can understand biases if these are served with data. Datasets are capable of creating a bias which affects the decisions the model makes from that point on.

Model governance enables models to be audited and examined for speed, accuracy, and drift during production. It neglects any issues of model bias or inaccuracy, permitting models with risks involved to function smoothly.

Use Cases of Model Governance

Here are a few cases listed below to analyse the importance of model governance:

As mentioned before, the most glaring instance of why model governance is crucial in finance, but other industries require model governance as well. Banking industry uses machine learning models for many different processes that can be operated manually like credit scoring, interest rate risk modelling, and derivatives pricing.

Credit Scoring

Credit scoring models aid finance/ bank industry to make decisions in the loan approval process by delivering predictive analysis information concerning the potential for default or delinquency. It helps the bank to determine the risk costing they should use for the loan.

Interest Rate Risk Modelling

Interest rate risk models surveil earnings exposure to a range of potential market conditions and rate change to measure risk. The purpose of the model is to provide an overview of the potential dangers of the account it is monitoring.

Derivatives Pricing

These models estimate the value of assets by delivering a methodology for determining the cost of new products as well as complex products without market observations readily available. It is helpful for both the banks and investors to determine whether a business is worth investing in or not.

Algorithmia's model governance features

Serverless micro-service architecture for machine learning, algorithmia makes it the fastest route from development to deployment. It allows organizations to govern their machine learning operations securely with a healthy machine learning lifecycle. It manages MLOps with access controls to secure and audit machine learning models in production. Model governance algorithmia's one of the benefits which ensures model accuracy by governing models and testing for speed, accuracy and drift.