Chatbot Integrated with Machine Learning Deployed in the Cloud and Performance Evaluation

Abstract

Recently human-machine digital assistants gained popularity and commonly used in question-and-answer applications and similar consumer-supporting domains. A class of more sophisticated digital assistants employing longer dialogs follow the trend, and there are several commercial platforms supporting their prototyping such as Google DialogFlow, Manychat, Chatfuel, Amazon Lex, etc. This paper explores cloud deployment of chatbots systems and their performance assessment methodologies. The performance measures includes system response delays and natural language processing capabilities. A case study platform supporting so-called deep-logic chatbots with long cycling capabilities is implemented and used for the assessment. To enable human-like conversations with a chatbot, huge training data, complex natural language understanding models are required and need to be adjusted and trained continuously. We explore implementation formats supporting auto scaling, and uninterrupted availability. In particular, we employ an architecture consisting of separate dialog management, authentication, and Natural Language Understanding (NLU) services. Finally, we present a performance evaluation of such loosely coupled chatbot system.