The Framework for ML Governance

Most companies don't have problems building and deploying algorithmic models, but they do struggle to effectively manage them in production. Maximizing the value of machine learning projects in the enterprise requires a robust MLOps program. But there's one key challenge: The problem MLOps sets out to solve isn't just about technology. It's also about process.

In this report, Kyle Gallatin defines a framework for ML governance--a comprehensive strategy to help your organization deliver real business value with your MLOps program. While MLOps provides a set of best practices and tools that let you deliver ML at scale, ML governance is how you control and manage those practices and tools.

This report shows infrastructure and operations (I&O) leaders and CTOs how to approach AI projects in a way that adds value from start to finish.

• Approach ML governance with a consistent framework that covers ML operations and ML development
• Dive deep into specifics for implementing governance throughout the ML life cycle
• Understand why governing the delivery and operations stages are the most difficult parts of a comprehensive ML governance strategy
• Explore ways to involve the right stakeholders to set up an ML governance program