A healthcare startup wants to build a proprietary image-classification model to detect a rare skin condition from photos. The team includes experienced ML engineers who need full control over model architecture, hyperparameters, and training code. Which Google Cloud offering is the most appropriate choice to develop and serve this model?
Vertex AI's custom training and prediction services let teams bring their own training application code, choose any framework, tweak hyperparameters, and deploy the resulting model on fully managed infrastructure. AutoML Vision is designed for users who prefer a no-code interface and offers limited control over model internals. The Cloud Vision API is a pre-trained service that cannot be re-trained on a rare-disease dataset. BigQuery ML supports SQL-based modeling on structured data and does not handle image inputs. Therefore, Vertex AI custom training best meets the startup's requirement for maximum flexibility and control.
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What is Vertex AI custom training and prediction?
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How does Vertex AI compare to AutoML Vision?
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What is Vertex AI, and why is it suitable for custom model training?
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Can Vertex AI handle image data, and how is it integrated for prediction?
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