We present a framework for specifying, training, evaluating, and deploying
machine learning models. Our focus is on simplifying cutting edge machine
learning for practitioners in order to bring such technologies into production.
Recognizing the fast evolution of the field of deep learning, we make no
attempt to capture the design space of all possible model architectures in a
domain- specific language (DSL) or similar configuration language. We allow
users to write code to define their models, but provide abstractions that guide
develop- ers to write models in ways conducive to productionization. We also
provide a unifying Estimator interface, making it possible to write downstream
infrastructure (e.g. distributed training, hyperparameter tuning) independent
of the model implementation. We balance the competing demands for flexibility
and simplicity by offering APIs at different levels of abstraction, making
common model architectures available out of the box, while providing a library
of utilities designed to speed up experimentation with model architectures. To
make out of the box models flexible and usable across a wide range of problems,
these canned Estimators are parameterized not only over traditional
hyperparameters, but also using feature columns, a declarative specification
describing how to interpret input data. We discuss our experience in using this
framework in re- search and production environments, and show the impact on
code health, maintainability, and development speed.

提出了一个框架，以简化创新的机器学习方法，以便将这些技术应用于生产环境中。该框架允许用户编写代码以定义他们的机器学习模型，并提供抽象层以指导开发人员将模型编写为有利于生产环境的方式。通过使用特征列，对模型进行参数化，同时提供了一组实用程序，旨在加快实验工作，以使开箱即用的模型灵活且适用于各种问题。