Implement a Helpdesk Chatbot with Dialogflow & BigQuery ML




Create a BigQuery dataset

Create a new table in BigQuery dataset

Build an ML model to predicts time taken to resolve an issue

Run the query to evaluate the ML model

Create a Dialogflow Agent

Import an IT Helpdesk Agent

Create a Fulfillment that Integrates with BigQuery

Implement a Helpdesk Chatbot with Dialogflow & BigQuery ML

1 godz. Punkty: 5


Google Cloud Self-Paced Labs


Wouldn’t it be awesome to have an accurate estimate of how long it will take for tech support to resolve your issue? In this lab you will train a simple machine learning model for predicting helpdesk response time using BigQuery Machine Learning. You will then build a simple chatbot using Dialogflow, and learn how to integrate your trained BigQuery ML model with your helpdesk chatbot. The final solution will provide an estimate of response time to users at the moment a request is generated.

The exercises are ordered to reflect a common cloud developer experience:

  1. Train a Model using BigQuery Machine Learning

  2. Deploy a simple Dialogflow application

  3. Use an inline code editor within Dialogflow for deploying a Node.js fulfillment script that integrates BigQuery

  4. Test your chatbot

What you'll learn

  • How to train a machine learning model using BigQuery ML

  • How to evaluate and improve a machine learning model using BigQuery ML

  • How to import intents & entities into a Dialogflow agent

  • How to implement custom Node.js fulfillment scripts

  • How to integrate BigQuery with Dialogflow


  • Basic concepts and constructs of Dialogflow. Click here for an introductory Dialogflow tutorial that covers basic conversational design and fulfillment using a webhook.

  • Basic SQL and Node.js (or any coding language) knowledge.

Solution Feedback/Lab Help

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