Pienso raises $2.1M to help non-programmers interact with machine learning models

Lucas Matney
Though the world could probably use a few more machine learning experts, there are a lot of non-programming specialists whose expertise could directly benefit ML models.

Though the world could probably use a few more machine learning experts, there are a lot of non-programming specialists whose expertise could directly benefit ML models.

That's the thinking behind the startup Pienso, an MIT spinoff which its founders hope can make the process of training machine learning models more accessible to non-technical people and allow companies to call on their existing expertise to better the insights they receive.

"How can we embed a domain expert who doesn’t necessarily have machine learning experience and capture their expertise and use it?" is the question CEO Birago Jones says that Pienso answers.

The Brooklyn-based startup announced today that it has closed $2.1 million in seed funding led by Eniac Ventures, with participation from SoftTech VC, Indicator Ventures and E14 Fund. The company is using this cash to grow the small team and start growing its customer base.

The company's core product, the Intelligent Development Environment is an end-to-end solution that includes features like Lens, which gives non-technical folk the ability to interact directly with machine learning algorithms.

For companies interacting with huge sets of complex data, machine learning helps them make sense of it all, but it can also pile on the costs due to the specific expertise needed. Pienso aims to solve what is often referred to as the human-in-the-loop problem, where human judgment is needed to gather feedback and strengthen ML models.

"In many cases researchers, analysts, and other knowledge workers do not have a computer science or advanced statistical background, so they rely on someone else to input their knowledge – unfortunately, often the nuance, context, and details get lost in that process," said Jones.

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