Thanks for you quick answer Andy! Unfortunately, I cannot share any information 
about the use cases (company policy) for the moment but, of course, if we 
publish something in the future, I will be glad to share our feedback with the 

Looking forward to this 1.0 release.

Best regards,

-----Mensaje original-----
De: Andy Christianson [] 
Enviado el: jueves, 05 de abril de 2018 17:08
Asunto: Re: MiNiFi C++ and Tensorflow future plans?

**This Message originated from a Non-ArcelorMittal source**

Hash: SHA1


> I was wondering what is the roadmap and future plans of MiNiFi C++ 
> agent regarding Tensorflow processors. With the three that are 
> mentioned in the article it is possible to classify images on edge but 
> I would like to know if other processors from TF will be included so 
> we can train a neural network on edge.
> Also, if you share with me a list of features that will be implemented 
> in the near futures, it would be very helpful.

Glad to hear you are interested in the project. The current plan is to cover 
the common edge inference use-cases. This includes general ML inference tasks 
as well as computer vision tasks including object classification, object 
detection (multiple output FlowFiles with bounding boxes + class), 
classification/anomaly detection in log files, time series anomaly detection 
(think temperature sensors).

The benefit of using MiNiFi - C++ rather than just a pure TensorFlow model is 
that all of the usual NiFi techniques like routing on attribute (i.e. object 
class or other ML-inferred metadata), sending to cloud endpoints (S2S to a NiFi 
instance, or ingest into a Kafka or MQTT queue, etc.), and arbitrary scriptable 
actions (ExecuteScript processor) are all fairly simple to do.

MiNiFi - C++ is a community-driven Apache project, so it ultimately will 
include any FlowFile -> tensor or tensor -> FlowFile processor that is 
developed to satisfy community requirements.

What use cases do you have in mind? MiNiFi - C++ is nearing its 1.0 release and 
as such most common use cases, including deep neural networks or even training 
on the edge (given sufficient resources, i.e. GPU) should be possible without 
too much custom programming effort. We'd like to get your input/feedback as a 
potential community participant so that MiNiFi will become more useful to 
everyone over time.


Andy I.C.
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