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Data Engineering, Prep, and Labeling for AI 2019 - They’re not wrong

Disclosure: We are not a Cognilytica client and we are not mentioned in this report.

In January Cognilytica published a report, “Data Engineering, Prep, and Labeling for AI 2019,” in which they delineate the requirements and hurdles of data preparation, as well as the growing need for properly annotated data as the AI industry evolves.

According to this report preparing data is more difficult, more time consuming, and more expensive than most organizations initially expect. Before an AI team can even get to labeling, the data itself needs to be cleaned up. We call it “getting your data house in order." For more about this topic check out Blueprint to Preparing your own ML Training Data.

Lack of data isn’t usually the problem.

Companies have plenty of data that can be prepared to train an AI model. The problem is that the data is disorganized and not ready for this purpose. The arduous task of cleaning and organizing training data is an important early step in the ML development process. Data scientists get stuck doing the bulk of this when companies try a DIY approach. This is both expensive and dissatisfying because data scientists take jobs to do interesting, challenging, and strategic work, not to draw boxes. This step is essential, time consuming, and can knock your ML project off track before it even begins.

Cognilytica understands this dilemma:

“the vast amount of time spent in a typical machine learning AI project is spent on identifying, aggregating, cleaning, shaping, and labeling data to be used in machine learning models.”

They also point out:

“Time is of the essence to train and operationalize models, and most organizations can’t afford to spend multiple months gathering, cleansing, and augmenting data, and then training their ML models, to only later realize they either have the wrong data or bad data.”

A Solution

These issues can both be remedied by working with what Cognilytica refers to as a 3rd party vendor, or “data labeling solution providers.” These vendors not only provide tools and coordination for an immense workforce, but also audit and verify the quality of annotations. According to Cognilytica,

“For every 1x dollar spent on Third-Party Data Labeling, 5x dollars are spent on internal data labeling efforts”

Companies are quickly wisening up to the opportunities afforded by offloading their data prep and the market is poised for growth.

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As a data labeling solutions provider, we have seen this growth first hand. Cognilytica predicts even more growth over the next few years, especially for 3rd party solutions like ours.

Alegion prepares training data for Fortune 1000 companies engaging in AI projects. We are a full-service AI data prep company. We provide our clients with a uniquely complete solution that includes highly-qualified project managers who can design tasks, and monitor and test progress. We have a global crowd, able to handle any size data training project at any level of security required.

Do you have training data preparation needs? Email us, we can help.