Explore the creation of production machine learning pipelines using TensorFlow Extended (TFX) in this Google I/O'19 conference talk. Dive into implementing TFX pipelines capable of processing large datasets for modeling and inference. Learn about data wrangling, feature engineering, detailed model analysis, and versioning. Discover how to implement a TFX pipeline and gain insights into current topics in model understanding. Explore key concepts such as data quality, validation, component configuration, dependency graphs, metadata stores, lineage, and warm starting. Understand the importance of model analysis, reusable components, and open-source orchestrators. Gain valuable knowledge on model understanding techniques, including data validation, slicing, and what-if analysis. Enhance your skills in developing robust machine learning pipelines for production environments.