Building Conversational Experiences with Dialogflow
Duration: 1 Day | Delivery Method: Instructor-led
This course provides a deep dive into how to create a chatbot using Dialogflow, augment it with Cloud Natural Language API, and operationalize it using Google Cloud tools.
- Identify the purpose and value of Dialogflow conversation engine and Google Cloud Platform tools.
- Learn how to build a custom chatbot with text and audio functionality in Dialogflow.
- Implement best practices when creating intents and entities.
- Understand how to use context and action fulfilment.
- Create a serverless backend for the chatbot using Cloud Functions.
- Augment the chatbot with sophisticated data processing and a high-performance storage backend.
- Deploy the chatbot as a security-enabled web application.
- Integrate the chatbot with Google Assistant.
This class is intended for the following:
- Individuals interested in learning how to create their own custom chatbot applications.
- Developers interested in getting hands-on experience with Google Cloud Platform tools to create a custom chatbot web application with authentication.
To get the most of out of this course, participants should have:
- Basic familiarity with programming concepts.
- Familiarity with Python and Node.js is a plus but not required.
- Students will not write code in the hands-on labs but the ability to read and understand code will be helpful.
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- This module opens with a discussion of conversation as the new UI, which is very quickly changing the way users communicate with businesses, employers, etc. It looks at some of the challenges when creating conversational agents that can handle natural language input, introduces Dialogflow, a tool to build smart conversational interfaces, and discusses how Dialogflow can address some of these concerns.
- In this module, learners will learn how to follow best practices when creating intents and entities for their virtual agents in a pizza-ordering scenario.
- In this module, learners will learn how to use context for carrying conversation awareness and how to use Cloud Functions to store orders on a database.
- In this module, learners will use what they have learned so far to build a brand new agent, applying the themes that are critical when deploying to production. They use several Google Cloud products to (1) extract keywords from a document and populate them into Cloud Datastore, (2) run scripts in Cloud Datalab to integrate this data into a Dialogflow agent, (3) deploy a webhook on App Engine, and (4) add HTTP basic authentication on their webhook code.
- In this module, learners get a glimpse of some of the upcoming features of Dialogflow.