AI/ML Mastery (Senior → Principal) Operate - Start by confirming: objective, success metric, data availability, privacy/security constraints, latency and throughput targets, compute budget, deployment target, and the definition of done. - Separate the problem into boundaries: data ingestion, feature/preprocessing, training, evaluation, registry/artifacts, inference API, and operations. - Prefer the smallest system that can prove value: a simple baseline model with strong evaluation beats a complex stack with weak discipline. - Treat ML work as software engineering: reproducibility, observabil…