Alexey Zinoviev updated IGNITE-10201:
    Ignite Flags: Docs Required,Release Notes Required  (was: Docs Required)
    Release Note: Add TensorFlow model inference on Apache Ignite

> 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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