The most streamlined and integrated method for deploying a model on Databricks leverages its native MLOps capabilities. The process begins by logging the trained model and its artifacts using MLflow during the training run. This captures the model, its dependencies, and metadata. The logged model is then registered directly into the Unity Catalog, which provides centralized governance, lineage, and access control. Finally, a Databricks Model Serving endpoint is created from the registered model version. This entire workflow is managed within the Databricks ecosystem, minimizing manual steps and abstracting away the underlying infrastructure, making it the easiest and most efficient process.

A: This process includes redundant manual steps. MLflow's logmodel function automatically handles packaging (pickling) and logging, making separate pickling and uploading to a volume unnecessary.

C: This describes a generic containerization workflow. It bypasses the integrated, simplified, and managed MLOps tools like MLflow and Databricks Model Serving, which is not the easiest path on Databricks.

D: This involves manually building a web service. Databricks Model Serving automates this entire process, providing a fully managed, scalable, and secure REST API endpoint without requiring custom Flask/Gunicorn code.

1. Databricks Official Documentation, "Manage model lifecycle in Unity Catalog": This document outlines the standard MLOps workflow on Databricks. The typical workflow section explicitly states the sequence: "Log the model to MLflow... Register the model with Unity Catalog... Deploy the model for batch or real-time inference." This directly supports the sequence described in option B.

Source: Databricks Machine Learning Guide > MLOps > Model lifecycle > Manage model lifecycle in Unity Catalog.

2. Databricks Official Documentation, "Log, register, and deploy a model with MLflow": This tutorial provides a code-based example of the end-to-end process. It demonstrates using mlflow..logmodel() to log the model, followed by mlflow.registermodel() to place it in the registry (Unity Catalog), and then points to creating a serving endpoint as the deployment step.

Source: Databricks Machine Learning Guide > MLflow > Log, register, and deploy a model with MLflow.

3. Databricks Official Documentation, "Model serving with Databricks": This document explains that the prerequisite for creating a serving endpoint is having a model registered in the MLflow Model Registry or Unity Catalog. It states, "After you log a model... you can automatically create a model serving endpoint for the model," confirming that serving is a direct step following model registration.

Source: Databricks Machine Learning Guide > Model Serving > Model serving with Databricks.