AI & ML Technology Updates

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How Alegion Guarantees Quality

This white paper shows how Alegion is able to guarantee quality through our...
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CV Case Study: Tracking Attentiveness & Emotion

This case study shows how leveraging high-precision data annotation empower...
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The Expense of Poorly Labeled Data

We take a good data set conducive towards modeling and distort the data in ...
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Cost Of Poorly Labeled Data

Download this complimentary white paper to learn what causes poor quality t...
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How to Improve Quality

Download this short complimentary guide to improve your data labeling quali...
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How to Achieve Quality Data Annotation

Alegion's Data Science Team put together a whitepaper on annotation guideli...
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5 Key takeaways from Trade show season

Trade show season has begun and these conferences continually demonstrate t...
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Alegion in WSJ & Forbes!

The Wall Street Journal and Forbes take notice that manually labeling data ...
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Agriculture is deep into machine learning

ML is revolutionizing agriculture. The benefit to growers is faster, more p...
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Survey Says: Outsource Your Data Labeling

Global survey of data scientists shows that organizations that outsource th...
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Supervised vs Unsupervised Learning

What we learned attending 5 AI/ML conferences in June! And a new “how to” ...
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Survey says: model confidence is built on a lot of training data

New survey reveals a concrete definition of “huge” in reference to 72% rep...
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The Primacy of Flexibility

To address both volume and complexity, a computer vision labeling and annot...
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ODSC East 2019

The AI space is maturing and training data is feeding the growth.
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Data Engineering, Prep, and Labeling for AI 2019 - They’re not wrong

Report delineating the requirements and hurdles of data preparation, as wel...
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We are making an impact

The rapid growth in enterprise ML projects has helped us to achieve our imp...
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A blueprint for preparing your own ML training data

Considering DIY training data? We created a downloadable blueprint that lay...