You are using Vertex AI and TensorFlow to develop a custom image classification model. You need the model's decisions and the rationale to be understandable to your company's stakeholders. You also wa

Generating and visualizing features and statistics with TensorFlow helps with data understanding, but doesn't directly explain *why* the model made a specific prediction or its rationale.

TensorFlow Profiler helps in optimizing model training performance by visualizing execution, but it does not provide explanations for the model's predictions or help identify biases.

Vertex Explainable AI's example-based explanations help stakeholders understand the model's rationale by showing similar examples from the training data, making decisions more interpretable and useful for identifying potential biases or issues, especially for image classification.

While Vertex Explainable AI's feature attributions are useful for understanding feature importance, example-based explanations often provide more intuitive insights into *why* an image was classified a certain way for stakeholders and identifying specific decision issues.