Search Results for "data integration"

...It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data. The easiest and most straight-forward way of using TensorFlow Serving is with Docker images. We highly recommend this route unless you have specific needs that are not addressed by running in a container. In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. ...

PyDenseCRF is a Python library that provides a wrapper around the implementation of fully connected Conditional Random Fields (CRFs) developed by Philipp Krähenbühl and Vladlen Koltun. The project allows developers and researchers to integrate Dense CRF inference into Python-based machine learning pipelines, particularly for computer vision tasks such as image segmentation and labeling. Conditional Random Fields are probabilistic graphical models used to model contextual relationships...