Clarifai is revolutionizing the way unstructured data is labeled

Clarifai

Clarifai, a leading independent artificial intelligence (AI) company, announces Clarifai Labeler (https://www.clarifai.com/label), a new way of labeling unstructured image, video and text data in its end-to-end AI platform. Clarifai has built one integrated tool for managing data annotation projects of any size. Customers can now label data faster and more accurately than ever before. Labeler seamlessly integrates within Clarifai’s platform so that users can manage the whole AI lifecycle in one place: labeling datasets, searching data using AI, training AI models and auto-scaling models in production.

For enterprises looking for additional help increasing productivity, Clarifai now offers a fully managed data labeling service (https://www.clarifai.com/services/data-labeling). Expert annotators, assisted by AI-automated tools, help companies reduce the complexities of managing labeling workforces. The new service greatly accelerates the time-to-value from AI.

“Customers have told us that labeling is one of the most tedious, costly, and time-consuming steps in creating high performing training datasets,” said Matt Zeiler, CEO and Founder of Clarifai, “Using our new labeling solution, it’s easy for teams to label, train, and deploy AI models, all from the same unified platform.”

Clarifai’s end-to-end platform for the AI lifecycle streamlines the model building process from data ingestion to model creation, all the way through to model deployment. Clarifai Labeler offers AI-assisted automation to prefill labels and speed up project completion. Using task management features designed for large human-in-the loop workforces, it’s seamless to assign labeling tasks to a distributed group and gain transparency into annotators’ work.

With these new advancements, Clarifai customers can use AI to make labeling data an order of magnitude faster than the traditional technologies out there. By augmenting human workforces with AI model recommendations, enterprises can reclaim their data scientists’ time and productivity while letting labelers accelerate the repetitive task of annotating data.

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