MLOps for Conversational AI with Rasa, DVC, and CML (Part I)

This is the first part of a series of blog posts that describe how to use Data Version Control (DVC), and Continuous Machine Learning (CML) when developing conversational AI assistants using the Rasa framework. This post is mostly an introduction to these three components, in the next post I'll delve into the code, and how to get everything connected for Rasa MLOps bliss. If you've not heard of Data Version Control (DVC), you've been missing out. DVC is an exciting tool from iterative.ai DVC extends git's functionality to cover your data wherever you want to store it, whether that is locally, on a cloud platform like AWS S3, or a Hadoop File System. Like git, DVC is language agnostic.