The upgrade to Snorkel Flow comes at a time when enterprises are looking to leverage AI across various data modalities. Whether it is structured or unstructured data, AI technologies are being applied ...

“So basically, when you upload a dataset to label in Snorkel Flow, you can now just get a push-button kind of first-pass auto labeling using the power of foundation models,” Ratner said.

Scale AI is a leader in data labeling for AI models. It’s an industry that, at its core, does what it says on the tin. The most basic example can be found in the thumbs-up and thumbs-down icons you’ve ...

Manually labeling data for AI is an extremely labor-intensive process. It can take weeks or months to label a few hundred samples using this approach, and the accuracy rate is not very good ...

Refuel Cloud revolutionizes the data labeling process – teams can simply describe in natural language how they want their data labeled, and not have to do the tedious work of labeling themselves ...

Dataloop competes against heavyweights in the data annotation and labeling space, including Scale AI, which has raised over $600 million in venture capital. Labelbox is another major rival, having ...

Landau says the company is going after places that are still using open-source or internal tools to do their own data labeling. So far, the company has raised $17.1 in seed and Series A funding ...

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make ...

Here are the top 5 best data labeling software for video and image annotation in 2023. (Photo : John Schnobrich / Unsplash) Data labeling software is crucial in developing artificial intelligence ...

Encord has published data to back up Landau’s claims. For instance, one study conducted in conjunction with Kings College London compared CordVision with a labeling program developed by Intel.

Refuel Cloud revolutionizes the data labeling process – teams can simply describe in natural language how they want their data labeled, and not have to do the tedious work of labeling themselves.