Use a single Databricks notebook to perform all the steps of the machine learning pipeline, including data preprocessing, feature engineering, model training, and model deployment.

Design a multi-step machine learning pipeline using Databricks notebooks and MLflow for experiment tracking and model management, and deploy the trained model as a Databricks model service.

Leverage a third-party machine learning platform to handle data preprocessing, feature engineering, and model training, and use Databricks notebooks for data exploration and visualization.

Implement a custom solution using a combination of Databricks notebooks, Apache Spark MLlib, and a custom model serving framework for model deployment, and use a custom monitoring system to track the performance of the deployed model.