Culture Amp has successfully implemented DevOps techniques, specifically using Buildkite, to create a reproducible, traceable, and scalable machine learning model training pipeline. This process is part of MLOps, where machine learning is integrated with DevOps methodologies, to automate and streamline the training and deployment of models, particularly in the realm of Natural Language Processing (NLP). Culture Amp employs a tech stack involving AWS services such as Fargate, S3, Athena, and SageMaker, alongside tools like dbt and Metaflow, to preprocess data, run training algorithms, and manage models. This approach allows for versioned training code, automated training runs, and human-in-the-loop evaluation to ensure model accuracy and relevance, particularly important in dynamically changing contexts like post-COVID employee engagement surveys. The deployment architecture involves creating SageMaker endpoints for production use, with each model tagged for tracking and error resolution. This pipeline not only supports Culture Amp's sentiment and topic classification models but also sets the stage for future expansions in applying NLP across various textual data in employee engagement and performance management.