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High-quality data may be the key to high-quality AI. with studies When we find that it is the organization of a dataset, not the size, that really impacts AI model performance, it is not surprising that there is an increasing focus on dataset management practices. according to some SurveysToday’s AI researchers spend much of their time on data preparation and organization tasks.
Brothers Vahan Petrosyan and Tigran Petrosyan felt the pain of having to manage so much data while training algorithms in college. Vahan went so far as to create a data management tool while earning his Ph.D. Research on image segmentation
A few years later, Vahan realized that developers — and even companies — would be happy to pay for similar tools. So the brothers founded a company, SuperAnnotateto build it.
“During the explosion of innovation in 2023 surrounding multimodal models and AI, the need for high-quality datasets has become more stringent, as each organization has multiple use cases requiring specialized data,” Fahan said in a statement. “We saw an opportunity to build an easy-to-use, low-code platform, like the Swiss Army Knife, for modern AI training data.”
SuperAnnotate, whose clients include Databricks and Canva, helps users create and track large sets of AI training data. The startup initially focused on software labeling, but now provides tools to fine-tune, replicate, and evaluate datasets.
Using the SuperAnnotate platform, users can connect data from on-premises and cloud sources to create data projects that they can collaborate on with their teammates. From the dashboard, users can compare the performance of the models against the data that was used to train them, and then deploy these models in different environments once they are ready.
SuperAnnotate also provides companies with access to a market of crowdsourced workers for data annotation tasks. Annotations are typically pieces of text that specify meaning or pieces of data that models are trained on, and serve as guideposts for models, “teaching” them to differentiate between things, places, and ideas.
To be frank, there It is several Reddit Topics About SuperAnnotate’s processing of the annotated data it uses, which is not fun. Explainers complain of communication problems, unclear expectations, and low wages.
For its part, SuperAnnotate claims that it pays fair market rates and that its requests from annotators are not out of the ordinary in the industry. We’ve asked the company to provide more detailed information about its practices and will update this piece if we hear back.
There are many competitors in the AI data management space, including startups like Scale AI, Weka, and Dataloop. San Francisco-based SuperAnnotate has managed to hold its own, however, as it recently raised $36 million in a Series B round led by Socium Ventures, with participation from Nvidia, Databricks Ventures, Play Time Ventures, and Defy.vc.
The new capital, which brings SuperAnnotate’s total raised to just over $53 million, will be used to augment its current team of about 100 people, for product research and development, and to grow SuperAnnotate’s customer base of about 100 companies.
“We aim to build a platform that is able to fully adapt to the evolving needs of organizations and offer extensive customization in data tuning,” Fahan said.
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