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An e-commerce organization wants to build a machine learning solution to categorize customer support tickets. The tickets contain unstructured data, including free-form text descriptions and uploaded product images showing damaged items.
The development team has minimal machine learning expertise and wants a solution that automatically handles feature engineering, architecture selection, and model training without requiring custom deep learning code.
Which Google Cloud approach should the organization use?
Train an AutoML forecasting model to predict categorical labels for incoming image files.
Deploy an unmanaged Apache Spark MLlib cluster on Dataproc to manually code custom convolutional neural networks.
Train custom models using Vertex AI AutoML for image and text data.
Use BigQuery ML to train a standard linear regression model on the raw text and image files.
Train an AutoML forecasting model to predict categorical labels for incoming image files.
Deploy an unmanaged Apache Spark MLlib cluster on Dataproc to manually code custom convolutional neural networks.
Train custom models using Vertex AI AutoML for image and text data.
Vertex AI AutoML is a suite of automated machine learning capabilities within Google Cloud designed to enable developers and data practitioners to train high-quality, custom machine learning models with minimal coding and without requiring deep machine learning expertise. It automates complex ML processes such as feature engineering, neural architecture search, hyperparameter tuning, and model evaluation.
For unstructured data such as raw images and unstructured natural language text, traditional SQL-based or tabular tools are insufficient. Vertex AI AutoML bridges the gap between pre-trained off-the-shelf APIs and custom coding, delivering custom models tailored to specific business categories without the operational overhead of manual model development.
Use BigQuery ML to train a standard linear regression model on the raw text and image files.
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