ML development often focuses on metrics, delaying work on deployment and scaling issues. ML development designed for production deployments typically follows a pipeline model with scaling and maintainability as inherent parts of the design. Robert Crowe and Charles Chen (Google) takes a deep dive into TensorFlow Extended (TFX), the open source version of the ML infrastructure platform that Google has developed for its own production ML pipelines.

Prerequisite knowledge

• Experience with ML development and software development

What you'll learn

• Discover issues and best practices for putting machine learning models and applications into production