The Definitive Guide to Conversational AI with Dialogflow and Google Cloud: Build Advanced Enterprise Chatbots, Voice, and Telephony Agents on Google » książka
Capturing conversation related metrics to store in BigQuery
Session Id
Date / time stamp
Sentiment score
Language & keyword
Platform
Intent detection
Building a platform for capturing conversation related metrics and redact sensitive information
Detecting user sentiment
Monitoring chat session & funnel metrics with Dialogflow , Chatbase or Actions on Google
Total Usage
The number of requests the intent was matched to and the percentage of all users that matched the intent.
Completion Rate & Drop off Rate / Drop off Place
User retention
Endpoint health
Discovery
Dialogflow Built-in Analytics
Monitoring metrics with Chatbase
Analytics on Actions on Google
Capturing chatbot model health metrics for testing the underlying NLU model quality
True positive - A correctly matched intent
False positive - A misunderstood request
True negative - An unsupported request
False negative - A missed request
Accuracy
Precision
Recall & fallout
F1 score
Confusion matrix
ROC curve
Improve the Dialogflow NLU model with built-in training
Summary
Reference
Lee Boonstra is a senior developer advocate at Google working with conversational AI. In this role she focuses on Dialogflow, Contact Center AI and speech technology. Lee is based in Amsterdam, the Netherlands, where she has been working with different technologies over the past 15 years, ranging from web/mobile, Ext JS, Sencha Touch, and Node.js, to conversational AI, Dialogflow, Actions on Google and Contact Centers.
Over the years she has helped many brands and enterprises to build and deploy conversational AI solutions (chatbots and voice assistants) at enterprise scale. She’s worn different hats from engineer to technical trainer to sales engineer to developer advocate. Prior to Google, Lee worked at Sencha Inc.
You can find Lee on online via the Twitter handle: @ladysign.
Build enterprise chatbots for web, social media, voice assistants, IoT, and telephony contact centers with Google's Dialogflow conversational AI technology. This book will explain how to get started with conversational AI using Google and how enterprise users can use Dialogflow as part of Google Cloud. It will cover the core concepts such as Dialogflow essentials, deploying chatbots on web and social media channels, and building voice agents including advanced tips and tricks such as intents, entities, and working with context.
The Definitive Guide to Conversational AI with Dialogflow and Google Cloud also explains how to build multilingual chatbots, orchestrate sub chatbots into a bigger conversational platform, use virtual agent analytics with popular tools, such as BigQuery or Chatbase, and build voice bots. It concludes with coverage of more advanced use cases, such as building fulfillment functionality, building your own integrations, securing your chatbots, and building your own voice platform with the Dialogflow SDK and other Google Cloud machine learning APIs.
After reading this book, you will understand how to build cross-channel enterprise bots with popular Google tools such as Dialogflow, Google Cloud AI, Cloud Run, Cloud Functions, and Chatbase.
You will:
Discover Dialogflow, Dialogflow Essentials, Dialogflow CX, and how machine learning is used
Create Dialogflow projects for individuals and enterprise usage
Work with Dialogflow essential concepts such as intents, entities, custom entities, system entities, composites, and how to track context
Build bots quickly using prebuilt agents, small talk modules, and FAQ knowledge bases
Use Dialogflow for an out-of-the-box agent review
Deploy text conversational UIs for web and social media channels
Build voice agents for voice assistants, phone gateways, and contact centers
Create multilingual chatbots
Orchestrate many sub-chatbots to build a bigger conversational platform
Use chatbot analytics and test the quality of your Dialogflow agent
See the new Dialogflow CX concepts, how Dialogflow CX fits in, and what’s different in Dialogflow CX