AI Is Only As Good As The Data @ "Labeling as a Service"

Companies want to use AI to build new business models, but getting high quality labeled data as the basis is the problem. For example, retail customers want to find out from hundreds of product reviews whether the ratings can be divided into categories, such as comments on the size, quality, durability, etc. of a product. But not everyone can really implement AI. Why? Unlike software, AI requires well-prepared, clear data, but only a few companies have the scope and the financial means to obtain or prepare such data. Data labeling / data annotation is often hard work and does not require a PhD. But so far, highly qualified data scientists spend 80% of their time doing it instead of building models.

The solution “Labeling as a service” makes it possible to outsource the process of cleaning, labeling and structuring the data. The data that should be processed accordingly can be images, videos, texts, etc. and is referred to as "input". The input can be converted into any desired output (classification, tagging, anonymization, etc.). A combination of AI and data labeling experts makes it possible to return the data in high quality quickly and inexpensively.

The company Canotic is an AI enablement platform that accelerates machine learning projects by using AI & humans to generate, structure, and label any type of data.

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