You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?
When building a predictive maintenance model using high definition images of bridges, a deep learning-based model created with TensorFlow is appropriate due to its capability to handle complex visual data. To explain the model output effectively, Integrated Gradients can be used, as it provides attributions of each pixel in the image to the final prediction. This method will help stakeholders understand which parts of the bridge images influence the model's decisions, allowing them to take appropriate actions based on these explanations.
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview#compare-methods
https://cloud.google.com/vertex-ai/docs/explainable-ai/overview this is right, your is deprecated!
Given the scenario of using high definition images as inputs for predictive maintenance on bridges, and the need to explain the model output to stakeholders, the most appropriate choice would be: C. Use TensorFlow to create a deep learning-based model, and use Integrated Gradients to explain the model output. Integrated Gradients is a method used to explain the predictions of deep learning models by attributing the contribution of each pixel in the input image to the final prediction. This would provide insights into which parts of the bridge images are most influential in the model's decision-making process, helping stakeholders understand why a particular prediction was made and allowing them to take appropriate action.
https://cloud.google.com/vertex-ai/docs/explainable-ai/overview
Use Integrated Gradients to explain the model output
Handling image input: Deep learning models excel in processing complex visual data like high-definition images, making them ideal for extracting relevant features from bridge images for defect detection. Explainability with Integrated Gradients: Integrated Gradients is a powerful technique specifically designed to explain the predictions of deep learning models. It attributes model output to specific input features, providing insights into how the model makes decisions. Visualization: Integrated Gradients can generate visual explanations, such as heatmaps, that highlight image regions most influential to predictions, aiding in understanding and trust for stakeholders.