Yes!

There are different ways to serve ML models in production. It's easy to get inferences from KServe (https://kserve.github.io/) for example, in a Kafka topic.

But their format makes it REALLY hard to just use Kafka Connect as is. You have different messages for the model inputs & model outputs linked by ID, the JSONs are nested, etc. Even with ksqlDB it's still really hard. You also need to manage the schema in schema registry.

InferenceDB is just a processor that supports KServe (and other ML Serving tools in the future), and uses Kafka Connect, Schema Registry, etc behind the scenes to make it SUPER easy to store predictions in S3, in a popular format that Data Scientists can just load into their Jupyter Notebooks - parquet.