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Hands-On Microservices with C#

You're reading from   Hands-On Microservices with C# Designing a real-world, enterprise-grade microservice ecosystem with the efficiency of C# 7

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781789533682
Length 254 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Cole Matt Cole
Author Profile Icon Matt Cole
Matt Cole
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Toc

Table of Contents (16) Chapters Close

Preface 1. Let's Talk Microservices, Messages, and Tools 2. ReflectInsight – Microservice Logging Redefined FREE CHAPTER 3. Creating a Base Microservice and Interface 4. Designing a Memory Management Microservice 5. Designing a Deployment Monitor Microservice 6. Designing a Scheduling Microservice 7. Designing an Email Microservice 8. Designing a File Monitoring Microservice 9. Creating a Machine Learning Microservice 10. Creating a Quantitative Financial Microservice 11. Trello Microservice – Board Status Updating 12. Microservice Manager – The Nexus 13. Creating a Blockchain Bitcoin Microservice 14. Adding Speech and Search to Your Microservice 15. Best Practices

Processing a machine learning request

Once a machine learning request has been processed and a reply message is available, we want to consume it right away. We will know of the completion because the performing microservice will be responsible for sending a response/completion message so that others may know of the status.

Here is our ProcessMLMessage function that will handle the incoming message. Depending upon the message type, it will display the appropriate information:

bool ProcessMLMessage([NotNull] MLMessage msg, [NotNull] MessageReceivedInfo mri)
{
Console.WriteLine("Received Machine Learning Message");
RILogManager.Default?.SendInformation("Received Machine Learning Message");
switch ((MLMessageType)msg.MessageType)
{
  1. Add a LayerType type of layer to the ConvNet:
case MLMessageType.AddLayer:
RILogManager.Default?.SendInformation("Adding a layer command...
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