Over the past year, Humanloop has developed innovative tools for training and deploying natural language processing (NLP) models, emphasizing the importance of domain expertise over technical machine learning knowledge. The platform facilitates rapid model training by centering data curation and annotation, allowing subject-matter experts like lawyers and doctors to efficiently contribute to AI model development. Humanloop's approach challenges traditional workflows by enabling dynamic adjustments to labeling taxonomies and fostering rapid prototyping with active learning, ultimately reducing the high failure rates of ML projects. The platform's focus on a data-centric but model-backed methodology highlights the interplay between data and model development, offering continuous learning and real-time improvements. This approach has helped diverse industry professionals harness AI's potential, paving the way for new applications and demonstrating the value of integrating domain expertise in machine learning processes.