Explore various methods of model serving with MLflow in this comprehensive one-hour video. Gain insights into both open-source MLflow and Databricks-managed MLflow approaches for serving models. Learn the fundamental differences between batch scoring and real-time scoring, with a particular focus on Databricks' upcoming production-ready model serving. Dive into topics such as prediction overview, vocabulary, online scoring, MLflow Model Server deployment options, container types, and scoring with JSON split-oriented format. Discover end-to-end ML pipeline examples, deployment plugins, and resources for MLflow Ray. Examine Keras/TensorFlow model formats and run model examples. Finally, explore model serving on Databricks, including production-grade model serving and the Databricks Model Serving launch.