The Evolution from Data Annotation to Smart Annotation Redefining AI Training

smart data annotation for waste management case study. credit - keymakr.

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.

keylabs automotive data annotation case study. credit - keylabs.
Keylabs automotive data annotation case study. Credit – Keylabs.

At the same time, providing KeySmart Annotation in response to market demand, we satisfy our partners’ needs with fast data annotation, powered by highly-qualified experts from different fields. For example, in our KeySmart Annotation teams we have radiologists as well as medical experts who ensure the quality of output data in specific projects. Or, a team of annotators who were specifically trained to work with datasets of different types of rabbish.

In other words, we’re ready to boost our annotation time with automated tools, but at the same time, we adapt our expertise to fit any project. The market of data annotation as we know it doesn’t exist anymore. Ready-made datasets and quick-to-get, off-the-shelf data annotation tools make the market easy to enter. The main power of KeySmart Annotation teams is human resource, responsible for all levels of data validation processes.

Data validation is a final, yet essential, phase of machine learning model training. It’s a process that scrutinizes your data, points out the weaknesses, and shows how well the model was trained. And Keymakr, with the team of over 400 highly skilled annotators, coupled with the proprietary data annotation platform, Keylabs, is well poised to lead this transformation in the data annotation market.

Multiplying automated data annotation with human expertise, companies can meet key KPIs, save time and enhance efficiency, thereby improving the quality of their output.

The future of data annotation lies not in its death, but in its evolution. The industry is set to move from basic data annotation to KeySmart annotation – a blend of automated techniques and the most effective human-in-the-loop use cases. It’s a new dawn for data annotation, and it’s called KeySmart Annotation.

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