Which potential findings would indicate that an ML-based solution is suitable for this scenario?

Explanations:

A lack of significant data points and attributes would hinder the ability of an ML model to learn effectively. For ML solutions to be suitable, a robust dataset with ample historical records is necessary to identify patterns and relationships.

If simple rule-based thresholds can predict crane failures, it indicates that a deterministic approach might suffice. ML is more suitable when the relationships in the data are complex and cannot be easily captured by simple rules.

Having failure data for only one type of crane model limits the generalizability of the ML model. Effective ML requires diverse and representative data across various crane types to develop accurate predictive models for all operational cranes.

High granularity of historical sensor data for the last three years provides a rich dataset for training ML models. More granular data allows for the capture of subtle patterns and trends, improving the model’s predictive capabilities.

The presence of data on the most common types of crane failures that the company wants to predict is crucial for the effectiveness of an ML-based solution. This data allows the model to learn from past failures, improving its accuracy in predicting future incidents.