Download our new whitepaper to learn about the many governance challenges that risk managers face—and why governing their analytics is essential in 2021.

Why risk managers need to improve governance of AI in 2021

Our 2021 state of enterprise machine learning report revealed that organizations are dramatically increasing their investments in AI and machine learning as they head into the new year. In 2021, more and more businesses will finally begin to realize the full potential of AI to deliver business results from the substantial data they’ve built up over the years.

However, as the volume of data increases—and more and more machine learning models are built from it—organizations are starting to bump up against their ability to manage it all.

Algorithmia, in partnership with AI Powered Banking founder and former senior executive at SunTrust and FleetBoston, machine learning– and financial industry–expert H.P. Bunaes, has just released a whitepaper detailing the many governance challenges that risk managers face—and why governing their analytics should be an essential task for them in 2021.

The whitepaper explores the latest governance challenges that chief risk officers (CROs) and risk managers face, demonstrating how to implement a better governance strategy for AI by using machine learning operations (MLOps).

Join our upcoming webinar to learn the 7 steps to effective AI governance

How to approach AI governance

In the whitepaper, Bunaes outlines a 7-step process to put an effective governance strategy in place. This process can help your organization implement best governance practices that will scale as model development and usage grows—while only fractionally increasing your costs.

The process consists of these steps:

  1. Assemble a complete catalog of models

  2. Put in place a flexible model risk management framework

  3. Build an efficient process for getting models deployed and integrated into legacy systems and data architectures

  4. Give IT the tools to operate, manage, and monitor the operational health of models in production

  5. Ensure there are mechanisms 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. Integrate data and model change management processes

  7. Develop standard audit reports and logs

Risk officers can use MLOps to implement such a governance framework, and the whitepaper shows you how to do so using Algorithmia’s leading MLOps platform.

Get started with AI governance today

Ready to start implementing AI governance with MLOps? Download the whitepaper to get Bunaes’ expert analysis of what risk managers need to know, and practical guidance on how to get started today.

Upcoming webinar: 7 steps to effective AI governance

Take actionable steps to improve your AI/ML governance in 2021. Join us for a webinar with Bunaes on March 16 to learn about the 7 steps to put an effective AI governance strategy in place. Register now.