[ https://issues.apache.org/jira/browse/IGNITE-10201?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Alexey Zinoviev resolved IGNITE-10201. -------------------------------------- Resolution: Fixed > ML: TensorFlow model inference on Apache Ignite > ----------------------------------------------- > > Key: IGNITE-10201 > URL: https://issues.apache.org/jira/browse/IGNITE-10201 > Project: Ignite > Issue Type: New Feature > Components: ml > Affects Versions: 2.8 > Reporter: Anton Dmitriev > Assignee: Anton Dmitriev > Priority: Major > Fix For: 2.8 > > > Machine learning pipeline consists of two stages: *model training* and *model > inference* _(model training is a process of training a model using existing > data with known target values, model inference is a process of making > predictions on a new data using trained model)._ > It's important that a model can be trained in one environment/system and > after that is used for inference in another. A trained model is an immutable > object without any side-effects (a pure mathematical function in math > language). As result of that, an inference process has an excellent linear > scalability characteristics because different inferences can be done in > parallel in different threads or on different nodes. > The goal of "TensorFlow model inference on Apache Ignite" is to allow user to > easily import pre-trained TensorFlow model into Apache Ignite, distribute it > across nodes in a cluster, provide a common interface to call these models to > make inference and finally perform load balancing so that all node resources > are properly utilized. -- This message was sent by Atlassian Jira (v8.3.4#803005)