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Conversational AI with Rasa

You're reading from   Conversational AI with Rasa Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077057
Length 264 pages
Edition 1st Edition
Tools
Concepts
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Authors (2):
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Xiaoquan Kong Xiaoquan Kong
Author Profile Icon Xiaoquan Kong
Xiaoquan Kong
Guan Wang Guan Wang
Author Profile Icon Guan Wang
Guan Wang
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: The Rasa Framework
2. Chapter 1: Introduction to Chatbots and the Rasa Framework FREE CHAPTER 3. Chapter 2: Natural Language Understanding in Rasa 4. Chapter 3: Rasa Core 5. Section 2: Rasa in Action
6. Chapter 4: Handling Business Logic 7. Chapter 5: Working with Response Selector to Handle Chitchat and FAQs 8. Chapter 6: Knowledge Base Actions to Handle Question Answering 9. Chapter 7: Entity Roles and Groups for Complex Named Entity Recognition 10. Chapter 8: Working Principles and Customization of Rasa 11. Section 3: Best Practices
12. Chapter 9: Testing and Production Deployment 13. Chapter 10: Conversation-Driven Development and Interactive Learning 14. Chapter 11: Debugging, Optimization, and Community Ecosystem 15. Other Books You May Enjoy

Deploying your Rasa assistant to production

Here we introduce how to deploy your Rasa assistant to production.

When to deploy

It is common to use the minimum viable product or MVP strategy during the product development process. MVP is all about building a usable product prototype that fulfills the key requirements in the most efficient and simple way and then iterating to fine-tune the product details.

In Rasa, the official recommendation is that a product can be put into production as an MVP as soon as it can handle the most important (but not every) "happy path" of Dialogue. It's recommended to use Rasa X to have early users test the product prototype. This is in order to continuously improve the model until the product prototype reaches the MVP standard and you are ready to deploy it to a production environment.

Deployment options

When we want to deploy a Rasa assistant on a large scale, we normally use solutions based on Kubernetes or OpenShift. Rasa...

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