Model Deployment Approaches

In the previous chapter, we looked at how we can utilize Databricks MLflow Model Registry to manage our ML model versioning and life cycle. We also learned how we could use the integrated access control to manage access to the models registered in Model Registry. We also understood how we could use the available webhook support with Model Registry to trigger automatic Slack notifications or jobs to validate the registered model in the registry.

In this chapter, we will take the registered models from Model Registry and understand how to deploy them using the various model deployment options available in Databricks.

We will cover the following topics:

• Understanding ML deployments and paradigms
• Deploying ML models for batch and streaming inference
• Deploying ML models for real-time inference
• Incorporating custom Python libraries into MLflow models for Databricks deployment
• Deploying custom models with MLflow and Model Serving
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