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Today we continue our Data-centric AI series joined by Shayan Mohanty, CEO at Watchful. In our conversation with Shayan, we focus on the data labeling aspect of the machine learning process, and ways that a data-centric approach could add value and reduce cost by multiple orders of magnitude. Shayan helps us define “data-centric”, while discussing the main challenges that organizations face when dealing with labeling, how these problems are currently being solved, and how techniques like active learning and weak supervision could be used to more effectively label. We also explore the idea of machine teaching, which focuses on using techniques that make the model training process more efficient, and what organizations need to be successful when trying to make the aforementioned mindset shift to DCAI.
With Watchful’s modern and interactive solution, the control of data labeling is placed back into the hands of data scientists and machine learning practitioners. Currently built to solve the data challenges faced with NLP, Watchful’s scalable data-centric approach allows anyone, from subject matter experts to MLOps engineers, to holistically explore, classify, annotate and validate any unique dataset to power AI initiatives and business processes. You can start using Watchful today by visiting watchful.io.