TensorFlowOnSpark (TFoS) was open sourced in Q1 2017, and it has gained strong adoption within the Spark community for running TensorFlow training and inferencing jobs on Spark clusters. At Spark Summit 2017, we explained how TFoS enables Python applications to conduct distributed TensorFlow training and inference efficiently by leveraging key built-in capabilities of PySpark and TensorFlow. In this talk, we cover the major enhancements of TFoS in recent months. We will introduce a new Scala API for users who want to integrate previously trained models into an existing Scala/Spark workflow. We will describe a new Python API for Spark ML pipelines to train all types of TensorFlow models, and conduct inference/featurization without any custom code. Additionally, we will cover the support for TensorFlow Keras API, and TensorFlow Datasets.