AI and ML Foundations for Solution Architects

Distinguish between AI, machine learning, deep learning, and generative AI and explain how each applies to enterprise contexts.

Describe the end-to-end ML model lifecycle: data preparation, training, validation, deployment, and monitoring.

Interpret key model evaluation metrics (e.g., accuracy, precision, recall, F1) and assess their business implications.

Identify the distinct architectural responsibilities of a solution architect versus a data scientist in AI projects.