Alegion secures $12 million to improve machines that train machines

Alegion secures $12 million to improve machines that train machines

Max Smolaks

August 14, 2019

2 Min Read

Data quality remains one of the biggest challenges facing AI projects

by Max Smolaks 14 August 2019

American data labeling startup Alegion has raised$12 million in a Series A funding round, led by RHS Investments.

The company uses a combination of clever algorithmsand human expertise to prepare datasets for use in machine learning projects.

Alegion said it will spend the money on expanding its Active Learning capabilities - a type of semi-supervised machine learning in which an algorithm is able to interactively query the human teacher when faced with classification issues. This should help minimize the amount of human time spent on boring and repetitive data labelling tasks.

“Artificial Intelligence’sinsatiable demand for accurate training data can’t be provided through humanpower alone,” said Hank Seale, founder of RHS investments. “Alegion’s abilityto supplement human effort with machine learning is strongly differentiating.”

In order to create accurate machine learningmodels, data scientists need increasingly large datasets – and anyinconsistencies or errors will have a direct impact on the quality of the modelscreated. According to Alegion’s own research, 96 per cent of data scientistshave encountered data quality and labeling challenges in their work.

Alegion annotates raw data so it can be understood by machines, with the brunt of the task handled by proprietary software, assisted by human experts. Its customers include AirBnB, Walmart and Microsoft, to name a few.

The company is headquartered in Austin,Texas, and has a development office in Kuala Lumpur, Malaysia.

“Just as assembly lines incorporate powertools and robotics to enable scale, ML model development will require machinestraining machines to achieve the highest levels of model confidence,” saidNathaniel Gates, CEO and founder of Alegion.

“Our customers can first leverage humanjudgement to train their model and then watch as newly trained machines areincorporated that allow unprecedented scaling.”

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