Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
AI and Machine Learning Adoption
Focuses on enterprise machine learning capabilities and adoption patterns. Topics include how to identify high value machine learning use cases, data readiness and feature engineering needs, model experimentation and training approaches, deployment and model operations practices, platform choices such as Vertex AI or equivalent services, governance and explainability, measurement of model performance in production, and organizational change required for successful adoption. Candidates should be able to describe common barriers to enterprise machine learning adoption and pragmatic steps to move from pilot to production while demonstrating measurable business value.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
Artificial Intelligence and Machine Learning Applications
Assess understanding of machine learning fundamentals and practical enterprise applications of artificial intelligence. Candidates should explain supervised and unsupervised approaches, model training and evaluation, data preparation and feature engineering, and operational concerns such as machine learning operations and monitoring. They should discuss generative artificial intelligence capabilities, natural language processing and computer vision use cases, how to measure business impact, and responsible artificial intelligence considerations including fairness, explainability, privacy, and governance.