Taking Machine Learning from Research to Production

We discuss the use of Machine Learning pipeline architectures for implementing production ML applications, and in particular we review Google's experience with TensorFlow Extended (TFX). An ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Most of the focus in the ML community is on research, which is exciting and important. Equally important however is bringing that research to production applications to solve real-world problems, but the issues and approaches for doing that are often poorly understood. An ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science.