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A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?
Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and most integrated manner.
Explanation of Options:
Option A: This method involves unnecessary steps like logging the model as a pickle object, which is not the most efficient path in a Databricks environment.
Option B: Logging the model with MLflow during training and then using MLflow's API to register and start serving the model is straightforward and leverages Databricks' built-in functionalities for seamless model deployment.
Option C: Building and running a Docker container is a complex and less integrated approach within the Databricks ecosystem.
Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to the native capabilities of Databricks and MLflow.
Option B provides the most straightforward and efficient process, utilizing Databricks' ecosystem to its full advantage for deploying models.