Artificial Intelligence and Machine Learning Strategy Questions

Evaluate the candidate's approach to identifying evaluating and implementing artificial intelligence and machine learning opportunities across products and operations. Topics include use case framing and prioritization based on business impact data availability and technical feasibility; data strategy feature pipelines and annotation; model development lifecycle and experimentation practices; model training infrastructure model serving and deployment patterns; continuous integration and continuous delivery for models and monitoring for performance and data drift; model governance including fairness explainability and privacy; cost and latency trade offs for inference and choices between cloud and edge; organizational models such as centralized platform teams versus embedded product teams; tooling and vendor selection; and metrics for measuring model return on investment. Interviewers should probe experience with model registries versioning deployment rollbacks A B testing and operationalizing models in production.