What measures should be implemented to make sure of data integrity and combat potential data poisoning attacks within the data pipelines feeding AI systems?
How do you detect and prevent model drift or bias in production AI?
How are teams be encouraged to experiment with new AI technologies and incorporate a “shift-left” mentality (embedding ethical considerations and testing practices early in the development lifecycle)?
What skills should senior engineers focus on to lead AI initiatives successfully?
What’s the best way to maintain cost controls when deploying AI at scale, especially if legacy infrastructure is involved?
What are the biggest infrastructure bottlenecks when scaling AI from pilot projects to enterprise-wide deployment?
What does it take to move from proof-of-concept AI testing tools to something reliable at scale across multiple teams or products?
How do you foster innovation while maintaining robustness and reliability in large-scale AI systems?
What are the biggest infrastructure challenges organizations face when scaling AI systems?
How do you choose between on-premises, cloud, and hybrid solutions to support large-scale AI workloads?
How do you balance performance, cost, and scalability when designing AI infrastructure?
How do you build trust in AI systems at scale, ensuring fairness, transparency, and accountability?
How do you build cross-functional alignment between data engineering, ML, and operations teams for scalable AI?
How do you foster innovation in AI development while maintaining reliability and security?
Where do you see the biggest opportunities for innovation in AI infrastructure today?
How should leaders approach scaling AI responsibly in their organizations?
What skills or roles are most critical for teams building AI systems at scale?
How do you ensure reproducibility of results when models are continuously retrained on streaming data?
What strategies have worked for testing privacy-preserving features without compromising user data integrity?
Could you share how you detect silent failures in large-scale AI pipelines before they reach end users?