Raza Habib, co-founder and CEO of Humanloop, shares his journey from skepticism to advocacy for weak labeling in machine learning. Initially doubting the efficacy of replacing manually annotated data with heuristic-based data from domain experts, Habib's perspective changed after engaging with NLP projects and learning about weak supervision. He discovered that weak supervision, pioneered by Alex Ratner and Chris Ré with the Snorkel package, involves creating labeling functions, using a Bayesian model to determine likely labels, and training a machine learning model on this dataset, which can rival small, manually labeled datasets in performance. Weak supervision optimizes expert time and allows for the creation of large datasets in under-resourced languages, proving beneficial in fields needing domain expertise, such as legal or medical data extraction. Despite existing tools like Snorkel and Skweak, the process of iterating on labeling functions remains challenging, leading Humanloop to develop a new tool to facilitate this. Habib emphasizes that weak supervision complements active learning, offering a solution to the cold start problem by quickly generating initial model data, and highlights the tool's ability to maintain data privacy while integrating with popular NLP packages.