Exam Professional Machine Learning Engineer topic 1 question 250 discussion

C) Data preparation in BigQuery. Ease of implementation with AutoML

Cost-Effectiveness: Leverages BigQuery for data storage and preprocessing, minimizing data movement costs. Utilizes Vertex AI's AutoML Tabular training, which is a pay-per-use service, reducing upfront costs compared to custom training environments. Rapid Iteration: AutoML Tabular automates feature engineering and model selection, allowing you to experiment with various configurations quickly. You can focus on refining feature engineering and interpreting model behavior based on AutoML's generated explanations.

You work at an ecommerce startup. You need to create a customer churn prediction model. Your company’s recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model? A. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn. B. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow. C. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model. D. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.

I went Option C

Option A: While logistic regression is interpretable, manual training in Vertex AI Workbench adds time and complexity. Options B and D: Deep neural networks can be powerful but often lack interpretability, making it challenging to understand model decisions. They also require more hands-on model development and infrastructure management.