Creating and maintaining a platform for reliably producing and deploying machine learning
models requires careful orchestration of many components---a learner for generating …

TensorFlow is a machine learning system that operates at large scale and in heterogeneous
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …

We describe TensorFlow-Serving, a system to serve machine learning models inside
Google which is also available in the cloud and via open-source. It is extremely flexible in …

TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using TensorFlow …

TensorFlow is an end-to-end open source platform for machine learning. It has a
comprehensive, flexible ecosystem of tools, libraries, and community resources that lets …

Companies are spending billions on machine learning projects, but it's money wasted if the
models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine …

TF. Learn is a high-level Python module for distributed machine learning inside TensorFlow.
It provides an easy-to-use Scikit-learn style interface to simplify the process of creating …

Machine learning libraries such as TensorFlow and PyTorch simplify model implementation.
However, researchers are still required to perform a non-trivial amount of manual tasks such …

In machine learning, models capture intelligence from data using algorithms implemented
on frameworks like TensorFlow. Models learn during the training phase; an iterative process …

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for
hardware-accelerated machine learning, suitable for both interactive research and …