  Effectively using Natural Language Processing (NLP) tools in under-resourced
languages requires a thorough understanding of the language itself, familiarity
with the latest models and training methodologies, and technical expertise to
deploy these models. This could present a significant obstacle for language
community members and linguists to use NLP tools. This paper introduces the CMU
Linguistic Annotation Backend, an open-source framework that simplifies model
deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB
enables users to leverage the power of multilingual models to quickly adapt and
extend existing tools for speech recognition, OCR, translation, and syntactic
analysis to new languages, even with limited training data. We describe various
tools and APIs that are currently available and how developers can easily add
new models/functionality to the framework. Code is available at
https://github.com/neulab/cmulab along with a live demo at https://cmulab.dev
