Without understanding or articulating these concepts properly, scaling with AI is nearly impossible. As organizations wish to scale with AI by multiplying (and sometimes complexifying) the number of projects going to production, they face new challenges:
Inefficiency - A rapidly growing stack of projects requires managing more people, more processes, and more technology.
Risks - Pushing projects into production involves a new set of risks that are very different from development. Data protection, opacity, or biases become tangible issues that can harm an organization’s reputation and financial performance.
Legal Pressure - These risks have been identified and discussed by regulators around the world for the last four years. Today, a growing number are proposing regulations to set new standards for AI Governance for the private sector, with potential fines for non-compliance.
Session Takeaways include: Clarification on the the connection between AI Governance, MLOps, and Responsible AI, (ii) Boundaries of each, (iii) What this means for implementing AI at Scale.