Google Professional Machine Learning Engineer
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Your team is deeply involved in numerous machine learning projects, with a significant focus on TensorFlow. Recently, you've developed a DNN model for image recognition that performs exceptionally well and is nearing production deployment. However, your manager has requested a demonstration of the model's inner workings to ensure transparency and trust among stakeholders. This presents a challenge as, while the model's performance is proven, its explainability is lacking. Given the constraints of needing a solution that does not require retraining the model and must be implementable within a tight deadline, which of the following techniques could assist in elucidating the model's decision-making process? Choose the two most appropriate options.
Your team is deeply involved in numerous machine learning projects, with a significant focus on TensorFlow. Recently, you've developed a DNN model for image recognition that performs exceptionally well and is nearing production deployment. However, your manager has requested a demonstration of the model's inner workings to ensure transparency and trust among stakeholders. This presents a challenge as, while the model's performance is proven, its explainability is lacking. Given the constraints of needing a solution that does not require retraining the model and must be implementable within a tight deadline, which of the following techniques could assist in elucidating the model's decision-making process? Choose the two most appropriate options.
Explanation:
Integrated Gradient is a powerful explainability technique tailored for deep neural networks, offering insights into the model's predictions by highlighting feature importance. It achieves this by computing the gradient of the model's prediction output with respect to its input features, all without altering the original model. This method adjusts inputs and calculates attributions to determine feature importances for the input image, utilizing tools like tf.GradientTape for gradient computation. The What-If Tool (WIT), while primarily designed for classification and regression models with structured data, can also be adapted for deep learning models to some extent, providing a visual interface to probe model behavior.
- A (PCA) is incorrect because PCA is a dimensionality reduction technique that transforms features into a new set of uncorrelated variables, not a tool for model explainability.
- D (LIT) is incorrect because the Language Interpretability Tool is specifically for NLP models, not image recognition tasks.
For more detailed information, refer to TensorFlow Core and Understanding Deep Learning Models with Integrated Gradients.