Deploy an End-to-End Chinese Knowledge-based Dialogue System in a MLOps fashion on AWS
Knowledge graph database structures data as nodes and vertex. It provides flexibility and allows diversity and heterogeneity in real-world data. However, managing and querying such databases requires professionality in understanding graph query language and the graph database itself. Luckily, natural language processing (NLP) technics can help both in creating graph databases and understanding users' queries.
In this post, we will build a system heavily relied on NLP that can extract information from unstructured texts and interpret users' natural language queries as graph query languages. Moreover, we will develop this system in an MLOps manner such that it can automatically update itself to cope with changes in data and schema.
Walkthrough of this post:
- Part I Algorithm: Introduce the model used for knowledge extraction and natural question understanding.
- Part II Deployment: An overview of how the train