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Implement a Helpdesk Chatbot with Dialogflow & BigQuery ML

Implement a Helpdesk Chatbot with Dialogflow & BigQuery ML

Horas 5 Créditos

GSP431

Google Cloud Self-Paced Labs

Overview

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

Prerequisites

  • 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.

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Puntuación

—/100

Create a BigQuery dataset

Ejecutar paso

/ 10

Create a new table in BigQuery dataset

Ejecutar paso

/ 10

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

Ejecutar paso

/ 20

Run the query to evaluate the ML model

Ejecutar paso

/ 20

Create a Dialogflow Agent

Ejecutar paso

/ 10

Import an IT Helpdesk Agent

Ejecutar paso

/ 20

Create a Fulfillment that Integrates with BigQuery

Ejecutar paso

/ 10