What is ML governance?

ML model governance is the overall process for how an organization controls access, implements policy, and tracks activity for models. It’s a must-have to mitigate risk for model failure, regulatory compliance and attack. Governance is the bedrock for minimizing risk to both to an organization’s bottom line and to its brand. Organizations with effective ML governance not only have a fine-grained level of control and visibility into how models operate in production, but they unlock operational efficiencies by integrating AI/ML governance policies with the rest of their IT policies.

With governance, organizations can understand all the variables that might affect model results, which helps them quickly identify and mitigate issues (such as model drift) that can degrade the accuracy of results and the performance of applications. These issues can directly impact the business’ bottom line and erode customer trust in the brand over time.

We lay out a 7 step framework for managing AI governance, that Algorithmia’s products support, in our latest whitepaper written by Algorithmia in partnership with H.P. Bunaes, founder of AI-powered banking and former CDO Suntrust bank.

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Why do organizations struggle with ML governance?

Governance is the #1 challenge organizations face in 2021 as they race to scale up their ML capabilities to remain competitive in rapidly digitizing markets. (Source: 2021 Trends in Enterprise ML report)

There are five main challenges that we see our customers face when it’s time to tackle ML governance for their organizations.

1. Best practices are unclear. We’re still in the early days of ML governance, and organizations lack a clear roadmap or prescriptive advice for implementing it effectively in their own unique environments.
2. Regulations are undefined. A changing and ambiguous regulatory landscape leads to uncertainty and the need for companies to invest significant resources to maintain compliance. Those that can’t keep up risk losing their competitive edge.
3. Existing solutions are manual and incomplete. Even organizations that are implementing governance today are doing so with a patchwork of disparate tools and manual processes. Not only do such solutions require constant maintenance, but they also risk critical gaps in coverage.
4. ML doesn’t easily integrate into existing IT policies. Effective ML governance requires collaboration with IT, but most organizations are still treating ML as a boutique initiative—making it difficult to integrate into more standardized enterprise IT processes and tech stacks.
5. Poorly governed ML presents risk to company assets. Companies implementing ML face risks to their brand and bottom line. Models that drift or are poorly understood can erode customer trust in a brand, while models that aren’t monitored risk failure in production.

What should organizations do to improve ML governance?

Organizations should implement an MLOps platform that can address the above ML governance challenges. Algorithmia is one of the only vendors that provide out of the box MLOps solutions with the required capabilities, otherwise organizations are forced to piece together and maintain their own solutions. Either way there are seven key areas to make sure you can support:

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1. A complete catalog of models, including model risk documentation, and descriptions of the sources of model data for training and predictions, and the destinations and uses of model outputs
2. A flexible model risk management framework, based on a risk gradient: higher-risk models get more validation, testing, and monitoring while lower-risk models get a lighter touch, with more of the responsibility delegated to the business unit or model developers
3. An efficient process for getting models deployed and integrated into legacy systems and data architectures
4. Tools by which IT can operate, manage, and monitor the operational health of models in production, getting model developers out of model operations
5. Tools to monitor model accuracy and data consistency that will generate alerts if model results or input data begin to drift or the quality of input data degrades
6. An integrated model and data change management process, so that changes to data or to models are properly tested and communicated to prevent nasty surprises
7. Standard audit reports and logs, so that examiners and auditors can review model results, a history of changes, and a record of data errors or past model failures and actions taken

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