TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks Heng-Tze Cheng† Zakaria Haque† Lichan Hong† Mustafa Ispir† Clemens Mewald†∗ Illia Polosukhin† Georgios Roumpos† D Sculley† Jamie Smith† David Soergel† Yuan Tang‡ Philipp Tucker† Martin Wicke†∗ Cassandra Xia† Jianwei Xie† †Google, Inc. ‡Uptake Technologies, Inc. ABSTRACT We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cuing edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the eld of deep learning, we make no aempt to capture the design space of all possible model architectures in a domain- spe- cic language (DSL) or similar conguration language. We allow users to write code to dene their models, but provide abstrac- tions that guide developers to write models in ways conducive to productionization. We also prov
