Translating Problems into ML Tasks

Formulate a precise, measurable definition of a 'violation' that can be operationalized using the available data.

Classify each violation type by the data modality (image, text, metadata) most likely to capture evidence for it.

Decompose the overall project goal into concrete ML sub-tasks (e.g., image classification, text classification) with specified inputs and outputs.

Identify gaps where no available modality provides a strong signal for a given violation type, and propose strategies to handle them.

State the key assumptions and limitations of framing meal quality monitoring as a set of ML tasks.