The Secret Weapon Behind Quality AI: Effective Data Labeling – insideBIGDATA

In this special guest feature, Carlos Melendez, COO, Wovenware, discusses best practices for The Third Mile in AI Development the huge market subsector in data labeling companies, as they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. The article addresses this trend and outlines how it is not really a commodity market, but can comprise different strategies for successful outcomes. Wovenware is a Puerto Rico-based design-driven company that delivers customized AI and other digital transformation solutions that create measurable value for government and private business customers across the U.S.

The growth of AI has spawned a huge market subsector and increasing interest among investors in data labeling. In the past year, companies specializing in data labeling have secured millions of dollars in funding and they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. Yet, what can be viewed as the third mile in AI development, data labeling, is also perhaps the most crucial one to effective AI solutions.

In very general terms, AI development can be broken down into four key phases:

Data Labeling is Not Created Equal

The third mile in AI development is where the action begins. Massive amounts of data is needed to train and refine the AI model our experience has showed us that a minimum of 10,000 labeled data points are needed and it must be in a structured format to test and validate it, and train the model to identify and understand recurring patterns. The labels can be in the form of boxes around objects, tagging items visually or with text labels in images or in a text-based database that accompanies the original data.

Once trained with annotated data, the algorithm can begin to recognize the same patterns in new unstructured data. To get the raw data into the shape it needs to be in, it is cleaned (errors fixed and duplicate information deleted); and labeled with its proper identification.

Much of data labeling is a manual and laborious process. It involves groups of people who must label images as cars, or more specifically, white cars, or whatever the specifics might be, so that the algorithm can go out and find them. As with many things that can take time, data labeling firms are looking for a quick fix to this process. Theyre turning to automated systems to tag and identify data-sets. While automation can expedite part of the process, it needs to be kept in check to ensure that AI solutions making critical decisions are not faulty. Consider the ramifications of an algorithm trained to identify children at the cross-walk of a busy intersection not recognizing those of a certain height because the data set used to train the algorithm didnt have data about these children.

Since data is the lifeblood to effective AI, its no wonder that investors are seeing huge growth opportunities for the market. Effective data labeling firms are in hot demand as companies look to find a faster path to AI transformation. To aggregate and label data not only takes months of time, but effective algorithms get better over time, so its a constant process. But when selecting a data labeling firm that automates the process, buyers must beware. Data labeling is not yet a commodity market, and there are many ways to approach it. Consider the following when determining how to accomplish your critical data labeling process:

As data continues to become the oil that fuels effective AI, its critical that getting it into shape for algorithm training is not treated as a commodity, but given the attention it deserves. Data labeling can never be a one-size-fits all task, but requires the expertise, customization, collaboration and strategic approach that results in smarter solutions.

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The Secret Weapon Behind Quality AI: Effective Data Labeling - insideBIGDATA