The EU AI Act delay isn't a reprieve. Here's how insurers should use the 16-month runway to build defensible AI governance before December 2027.
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.
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.
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.
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.
As a model builder, ask yourself, "What exactly is systems engineering, and how does it apply to my life as an AI practitioner?"
Learn more about synthetic data, what it is, where it is useful, and where it can be harmful. Here are tips to assess the quality of synthetic data for your AI use cases.
