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Codeless Deep Learning with KNIME

You're reading from   Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800566613
Length 384 pages
Edition 1st Edition
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Authors (3):
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Kathrin Melcher Kathrin Melcher
Author Profile Icon Kathrin Melcher
Kathrin Melcher
KNIME AG KNIME AG
Author Profile Icon KNIME AG
KNIME AG
Rosaria Silipo Rosaria Silipo
Author Profile Icon Rosaria Silipo
Rosaria Silipo
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
2. Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform FREE CHAPTER 3. Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform 4. Chapter 3: Getting Started with Neural Networks 5. Chapter 4: Building and Training a Feedforward Neural Network 6. Section 2: Deep Learning Networks
7. Chapter 5: Autoencoder for Fraud Detection 8. Chapter 6: Recurrent Neural Networks for Demand Prediction 9. Chapter 7: Implementing NLP Applications 10. Chapter 8: Neural Machine Translation 11. Chapter 9: Convolutional Neural Networks for Image Classification 12. Section 3: Deployment and Productionizing
13. Chapter 10: Deploying a Deep Learning Network 14. Chapter 11: Best Practices and Other Deployment Options 15. Other Books You May Enjoy

Building a Web Service with the REST Interface

In this section, you will learn how to build REST services using the KNIME software. As a practical example, we will walk through the deployment workflow of the sentiment analysis example of Chapter 7, Implementing NLP Applications.

The KNIME Server REST API offers an interface for non-KNIME applications to communicate with KNIME Server via simple HTTP requests. The main benefit of RESTful web services is the ease of integration of the application into the company IT landscape. Self-contained and isolated applications can call each other and exchange data via the REST interface. In this way, it becomes easier to add new applications to the ecosystem.

Any workflow uploaded on KNIME Server is automatically available via the REST API. This allows you to seamlessly deploy KNIME workflows as web services via the REST API and integrate them into the infrastructure of your data science lab.

In the sentiment analysis example, we want...

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