The Framework for ML Governance

Chapter 4. Putting It All Together

We’ve established that companies are struggling to get value out of their AI and that a comprehensive ML governance implementation is how to achieve that value. While MLOps has become ubiquitous (even if only in name), taking it one step further to manage your MLOps with ML governance is the next stage of maturity for enterprise ML.

ML governance isn’t nearly as much of a buzzword as “AI” or “MLOps,” but it is by far the most important component for delivering value with ML. It is the final step to making ML a standard part of any organization. The first companies to implement standardized ML governance will have a once-in-a-lifetime opportunity to dominate ML in their business vertical.

Getting Value from Your ML with ML Governance

MLOps is the set of best practices and tools that allow you to deliver ML at scale. ML governance is how you manage those practices and tools, democratizing ML across your organization through nonfunctional requirements like observability, auditability, and security. As ML companies mature, neither of these are “features” or “nice-to-haves”—they are hard requirements that are critical to an ML strategy.

The value of a comprehensive governance implementation is inherent. Through effective management and controls, you unlock better, faster, and more secure ML. While some governance features may sound vague or high level, they’re components of software and ML alike. Governance drives:

• More accurate ML through ...