What is a cascade deployment strategy for ML models and when would you use it?

A cascade deployment strategy involves deploying multiple models in a sequential manner, where the output of one model can dictate the subsequent model's execution. This is particularly useful in scenarios where models serve different purposes or levels of complexity. For example, the first model could filter data, while subsequent models perform more complex tasks such as classification or regression. This approach allows for gradual testing of models in production, reducing the risk of widespread failure. It's advantageous in environments with high variability in input data, enabling you to leverage simpler models for less complex cases while reserving more heavy-duty models for intricate predictions.