When selecting a labeling provider, make sure your provider in question has reliable ways to test the labelers’ expertise and measure labeling accuracy.Choose between external or internal teams and leverage pros and cons of each one to get the highest quality results.The report further lays out a number of recommendations for those in need of data labeling: The most important aspect of a successful labeling endeavor is dependable and easily scalable production pipelines, states the report.
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Gartner concludes that human-in-the-loop models are of particular benefit because they do not require deep domain knowledge by the parties seeking labeling services. The report explicitly states that many fields like search engine optimization, autonomous vehicles, Natural Language Processing, and e-commerce owe their success to human-handled data labeling. This ongoing demand is the result of both speedy growth of the AI industry itself, as well as the latest integration of new AI products into traditional business domains, from commercial shipping to agriculture. The report stipulates that the need for quality training data has skyrocketed over the past several years as more clients seek fast, reliable, and cost-effective labeling solutions.
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The report lists a handful of companies as today’s leaders of the data-labeling market – among them are Scale AI, Toloka, Appen, CloudFactory, Amazon MTurk, and Playment.Īccording to Gartner, these companies use both internal and external labelers to offer classification, segmentation, transformation, and augmentation procedures utilized to prepare training data for ML algorithms. Gartner identifies data labeling as one of the key factors responsible for the ongoing evolution of AI technology and rapid AI-powered product development. Gartner, a Connecticut-based technology consulting company with a well-established reputation for conducting research and gauging highly specialized industries, put out a new Hype Cycle report earlier this year.Ī whole section of the report is dedicated to data management and provides an in-depth examination of how the world of today has come to rely heavily on innovations in data science and Machine Learning (ML).