Insights
The Dimensions of Enterprise AI Governance: A Focus on Model Lifecycle Management
Explore how structured model lifecycle management turns governance principles into an operational reality, helping to guide AI development from design through retirement with control, transparency and trust.
AI Ethics & MLOps - Go Fast, Without Breaking Transparency
Explore how MLOps can ensure AI ethics and transparency in your organisation in "AI Ethics & MLOps - Go Fast, Without Breaking Transparency." This blog by Ben Saunders delves into the importance of integrating ethical considerations and governance into the machine learning lifecycle. Learn how MLOps frameworks can help build, deploy, and manage AI models that are reliable, transparent, and compliant with legal standards, fostering trust among customers and regulators while accelerating AI adoption.
The IP Dilemma in the Age of AI: Protecting Creators While Advancing Technology
In this blog, Ben Saunders explores the IP dilemma in the age of AI, focusing on the tension between content creators and AI companies over the use of intellectual property in training AI models. It discusses the challenges of balancing innovation with creators' rights, proposed solutions like digital watermarks and licensing, and the potential of data ownership and consent management as a path forward for fair compensation and ethical AI development.
Building the Business Case for AI & ML in Your Business
Discover how to make a compelling business case for AI and ML adoption in your organisation.