According to Databricks, batch deployment is the most common way of deploying machine learning models, accounting for approximately 80-90% of all deployments. This means running predictions from a model and saving them for later use. For live serving, results are saved to a low-latency database that will serve the predictions quickly. Alternatively, predictions can be stored in less performant data stores.
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Tomasz Zmarzly
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