How do you ensure AI systems remain performant under high-volume, real-time workloads?
How do you ensure AI systems remain performant under high-volume, real-time workloads?
What leadership strategies are most effective for driving AI adoption and scaling innovation responsibly?
Which skills should senior engineers and architects prioritize to manage AI systems at scale?
How do you maintain innovation without compromising operational stability?
Can AT/AI only focus on how the software works and NOT so much about the data used when customers work?