Quick Ways to Build Machine Learning Datasets

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There was a time when working with big data was not technically possible because our computing capabilities couldn’t handle the amount of information that was involved.  Boy, have times changed. Today, the explosion of digital data that is available to us, coupled with astonishing advancements in computing power, has fueled excitement about transformative technologies like artificial intelligence. But even with all this data and technology at our fingertips, many companies struggle with making artificial intelligence a reality.

The challenge is no longer about getting enough data, it’s about getting the right data. After all, artificial intelligence is only as smart as the data it learns from. What organizations really need as they develop their AI capabilities is an accurate foundation on which to build and train their machine learning algorithms; they need really great datasets.

A dataset is a collection of data points that corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the dataset in question. A great dataset lays the groundwork for machine learning.

We’ve developed a brief overview for companies looking to develop datasets for their machine learning projects. Whether it’s building your own from the ground-up, or sourcing data from the right inputs, this brief whitepaper provides some tips on building datasets so you can deliver on the promise of AI.

Building Machine Learning Datasets​

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(NASDAQ: INOD) Innodata is a leading data engineering company. Prestigious companies across the globe turn to Innodata for help with their biggest data challenges. By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of over 3,000 subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of digital data and ubiquitous AI.


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