Deploying ML models for batch and streaming inference

This section will cover examples of deploying ML models in a batch and streaming manner using Databricks.

In both batch and streaming inference deployments, we use the model to make the predictions and then store them at a location for later use. The final storage area for the prediction results can be a database with low latency read access, cloud storage such as S3 to be exported to another system, or even a Delta table that can easily be queried by business analysts.

When working with large amounts of data, Spark offers an efficient framework for processing and analyzing it, making it an ideal candidate to leverage our trained machine learning models.

Note

One important note to remember is that we can use any non-distributed ML library to train our models. So long as it uses the MLflow model abstractions, you can utilize all the benefits of MLflow’s Model Registry and the code presented in this chapter.

...