On October 10th, our team hosted a live webinar--Simple Distributed Deep Learning Model Inference--with Xiangrui Meng, Software Engineer at Databricks. Model inference, unlike model training, is usually embarrassingly parallel and hence simple to distribute. However, in practice, complex data scenarios and compute infrastructure often make this "simple" task hard to do from data source to sink. In this webinar, we provided a reference end-to-end pipeline for distributed deep learning model inference using the latest features from Apache Spark and Delta Lake. While the reference pipeline applies to various deep learning scenarios, we focused on image applications, and demonstrated specific pain points and proposed solutions.