This document discusses challenges in deploying machine learning models for scoring in streaming applications using Apache Spark. It describes how ML Pipelines and Structured Streaming in Spark can be used to build an application that monitors web sessions for bots in real-time. However, there are issues with two-pass transformers and handling invalid data in Spark 2.2. Spark 2.3 includes fixes that allow most transformers and models to work for both batch and streaming scoring, and improves handling of invalid values. The talk provides tips on updating pipelines to work with streaming and testing them.