What should you consider when teams tell you they’ve got AI “covered”? To increase your chances of success with AI, align your organization and projects around the following actions.
Learn about the goals of information theory, define the differences between metrics and divergences, explain why divergences are the wrong choice for monitoring, and propose better alternatives.
Many of the AI-based innovations used by enterprises are from specialty technology vendors. Get answers from a general counsel about why formal governance is critical to everyone's success with AI.
When is "easy" too good to be true? Learn more about the fine line between automating business operations and automating their governance.
Here is the supplement to our episode about non-parametric statistics. Learn from sample tests using Python 3.9 and popular scientific computing libraries.
The outcomes of the AI safety debate will directly affect your organization and your corporate responsibilities. Few enterprises have established a dedicated AI governance function, but with regulation taking shape and the business impact growing, how this function will be structured, tasked, and resourced are near-term questions.
The success of AI systems – effectiveness, safety, return on investment – depends on the right people coming together from across the business. Discover the roles that make this happen.
Well-designed AI governance can increase the quality of AI systems and speed up their development while also mitigating or even avoiding risks. It increases ROI in a crucial area of technology research and development.
Learn about loss functions and how machine learning models are constructed and "trained".
A common area of confusion in data science is how monitoring and governance are related to one another. Let's explore what is missing from MLOps monitoring that is essential for model governance.