Explore governance of foundation models and generative AI products. Learn about evaluating and governing these technologies in third-party management and model risk contexts.
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.
As a model builder, ask yourself, "What exactly is systems engineering, and how does it apply to my life as an AI practitioner?"
In episode 5 of the AI Fundamentalists podcast we discussed 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.
As the public becomes more aware of generative AI models, thanks in part to tools like OpenAI's ChatGPT, there has been a surge of fear and concern regarding AI regulation and governance. In this article, we put that fear into context and offer actionable steps toward governance, drawing from historical lessons.
What’s your mindset when building an AI model? Christoph Molnar, a statistician and machine learning expert, explains that our approach to factors such as interpretability and uncertainty is what takes our models beyond mere performance.
What is data management's role in machine learning and AI model systems? We illustrate the importance and process of establishing data controls and data quality prior to building these systems.
The increasing concerns about AI and the need for regulations are prevalent. However, focusing on regulations specific to LLMs can miss the bigger picture of best practices.
