How can a machine learning model be deployed in Databricks?

The deployment of a machine learning model in Databricks is best achieved by utilizing MLflow to register and deploy the model. MLflow is an open-source platform that streamlines the workflow for machine learning projects, making it easier to manage the entire machine learning lifecycle, which includes experimentation, reproducibility, and deployment.

By leveraging MLflow, users can track experiments, manage models, and automate the deployment process. It allows for registering a model after training, which facilitates easy version control and ensures that the correct model is used across different environments. MLflow supports deployment to various targets, including web services, making it a versatile option for deploying models in production settings. This integration within Databricks enhances collaboration and efficiency, as it centralizes the deployment and monitoring process.

The other options do not align with best practices for machine learning model deployment within the Databricks environment. Exporting a model as a CSV file is not a viable method for deployment since CSV is typically used for storing tabular data, not for model serving. Utilizing local machine resources may lead to scalability and performance issues, as cloud infrastructure provides more robust options for dealing with production workloads. Lastly, manually coding deployment scripts is often less efficient and prone to errors compared to using a standardized tool