In an era where the majority of businesses are engaged in a race to automate data annotation, we at Keymakr have found a fresh route to success in delivering projects for our partners.
It's true, employing a human-in-the-loop (HITL) approach to data annotation may be more expensive in the short term. However, when talking about cost, it isn't solely about money. Quality and time are just as significant. The bulk of the industry is focusing on time efficiency, adding an automation component to the data annotation process. We, however, see the future a bit differently.
Having been in the data annotation sector since 2015 and having successfully completed more than 1500 projects, we are placing our bets on a combination of automation and human expertise. The journey through these projects has sharpened our insight and enabled us to foresee the future of data annotation, as a complex decision provided by KeyMakr (service component) and KeyLabs (tool component), KeySmart Annotation. This is a blend of the best automated techniques and the most effective HITL scenarios in data validation.
Previously, the manual labor required to improve AI models was both time-consuming and expensive. Today, the use of pre-existing models such YOLO8, SAM and unsupervised learning techniques for automated data annotation, as well as usage of synthetic data, has become the industry standard, significantly reducing the manual workload. The challenge then is to maintain a balance between automated and manual solutions, as poor fault tolerance can lead to performance issues.
As the demand for advanced AI technology increases, industries should shift from data annotation towards automated solutions that aim at labeling your dataset more quickly to data validation which is more accurate. Using human-in-the-loop data validation checks ensures high-end competencies, making information more useful for use by machines. There's no doubt that machines can’t teach machines precisely. Such data is biased.