You need to build classification workflows over several structured datasets currently stored in BigQuery.
Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving.
What should you do?
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A. B. C. D.B.
BigQuery ML supports supervised learning with the logistic regression model type.
https://cloud.google.com/bigquery-ml/docs/logistic-regression-predictionThe most suitable option for building classification workflows over several structured datasets stored in BigQuery without writing code for exploratory data analysis, feature selection, model building, training, hyperparameter tuning, and serving is option A: Configure AutoML Tables to perform the classification task.
AutoML Tables is a cloud-based, fully managed service provided by Google Cloud that allows users to build and deploy machine learning models on structured data without any coding. It enables users to perform tasks like data preparation, model training, hyperparameter tuning, and deployment through a simple graphical user interface.
Here is a breakdown of how AutoML Tables can help complete the required steps:
Exploratory Data Analysis: AutoML Tables provides a data exploration interface that allows users to visualize and analyze data without any coding. This interface includes various statistical metrics, data distributions, and visualizations that enable users to understand their data better.
Feature Selection: AutoML Tables has built-in feature selection algorithms that can help identify the most relevant features for model training. These algorithms use various techniques like correlation analysis, mutual information, and feature importance scores to rank features based on their importance.
Model Building: AutoML Tables provides a variety of machine learning algorithms like deep neural networks, decision trees, and gradient boosting machines that users can choose from. Users can also customize their models by adjusting the architecture, regularization, and optimization algorithms.
Training: AutoML Tables can automatically split data into training, validation, and testing sets and train models using the selected algorithms. It also performs automatic hyperparameter tuning to optimize the model's performance.
Hyperparameter Tuning: AutoML Tables automatically tunes the model's hyperparameters using techniques like Bayesian optimization and grid search. This helps optimize the model's performance without any additional effort from the user.
Serving: Once the model is trained and optimized, AutoML Tables can deploy it to a scalable and secure endpoint that can be accessed through REST APIs. This allows users to integrate the model into their applications without any additional infrastructure setup.
In summary, using AutoML Tables to perform the classification task would provide a comprehensive and efficient solution to building classification workflows over several structured datasets stored in BigQuery without writing any code.