Serverless and Recurrent Neural Networks with Fn, GraphPipe and TensorFlow by Ralf Mueller


The last article First steps in serverless with marked the start of my journey into serverless computing. My first proof of concept in this area was quite promising so I have decided to continue on this path and do a couple more experiments. I have a set of use cases in mind where serverless architectures might be beneficial for certain integration scenarios that include Systems, People and Developers.


In this article I’m going to explore the use of modern Machine Learning and AI techniques in the context of serverless computing. I’m putting together an example that does the following:

· Function will be invoked with a conforming event. The vigilant reader might notice that I’ve been using CloudEvents in my previous example. This is not by accident, I’m envisioning an architecture that is based on standards and CloudEvents seems a natural choice here for multiple reasons; it is part of the Cloud Native Computing Foundation(although in Sandbox status at the time of this writing), it’s a simple but extensible data format, etc.

· Function will extract the Data portion of the CloudEvent and then calls into a Machine Learning model for scoring.

· Function will create a CloudEvent based response with the result of scoring against the Machine Learning Model.

As with my previous article, this is a very simple and contained use case. However it should give some ideas on what can be done in a larger context. Also, since I’m still a newbie in both the Go programming language and serverless, I’d like to keep the examples as small and simple as possible for the moment. Read the complete article here.


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Technorati Tags: PaaS,Cloud,Middleware Update,WebLogic, WebLogic Community,Oracle,OPN,Jürgen Kress


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