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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?
AutoML Tables is a service that allows you to automatically build and deploy state-of-the-art machine learning models on structured data without writing code. You can use AutoML Tables to perform the following steps for the classification task:
Exploratory data analysis: AutoML Tables provides a graphical user interface (GUI) and a command-line interface (CLI) to explore your data, visualize statistics, and identify potential issues.
Feature selection: AutoML Tables automatically selects the most relevant features for your model based on the data schema and the target column. You can also manually exclude or include features, or create new features from existing ones using feature engineering.
Model building: AutoML Tables automatically builds and evaluates multiple machine learning models using different algorithms and architectures. You can also specify the optimization objective, the budget, and the evaluation metric for your model.
Training and hyperparameter tuning: AutoML Tables automatically trains and tunes your model using the best practices and techniques from Google's research and engineering teams. You can monitor the training progress and the performance of your model on the GUI or the CLI.
Serving: AutoML Tables automatically deploys your model to a fully managed, scalable, and secure environment. You can use the GUI or the CLI to request predictions from your model, either online (synchronously) or offline (asynchronously).
[AutoML Tables documentation]
[AutoML Tables overview]
[AutoML Tables how-to guides]