Topic 1 Question 197
You work as an analyst at a large banking firm. You are developing a robust scalable ML pipeline to tram several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible. What should you do?
Use Tabular Workflow for Wide & Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models
Use Cloud Composer to build the training pipelines for custom deep learning-based models
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コメント(2)
- 正解だと思う選択肢: C
TabNet models are inherently more interpretable than deep neural networks or XGBoost models due to their attention mechanism. This aligns with the primary focus on interpretability.
👍 1pikachu0072024/01/12 - 正解だと思う選択肢: C
according to the documentation: "TabNet uses sequential attention to choose which features to reason from at each decision step. This promotes interpretability and more efficient learning because the learning capacity is used for the most salient features."
👍 1winston92024/01/13
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