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https://issues.apache.org/jira/browse/IGNITE-10201?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alexey Zinoviev resolved IGNITE-10201.
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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.
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