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What is the underestimated technical challenge in building Levity that is not easily grasped by those who lack AI expertise?

Thilo Huellmann

Co-founder & CTO at Levity

There are many hard things. On the machine learning side specifically, it's not as much about the ML itself because we're just applying what is already out there, proven, and available open source. Text and image classification is not a hard machine learning problem. 

What’s hard is everything around it. Putting everything into production and covering every step, from having some raw data somewhere to having a solution that does the task that you want to automate end-to-end.

From a cloud infrastructure point of view, it's also quite challenging. Let's say, if I'm a data scientist in a small team of a mid-sized company or even a large company, perhaps I run a few dozen models. But we have to build something that can support tens of thousands of concurrently deployed models that need to be available at all times because requests and prediction requests can come anytime. These models, especially in the text space, have become very large recently which makes it a challenge to deploy them in a cost-efficient way.

That's one of the machine learning-related things that we need to solve because we also want to make it possible for small- and medium-sized companies to use this technology. We cannot charge these companies 10,000 Euros a month for that. If we could, then it wouldn't be a problem, we would just throw money at the problem. But we have to build the infrastructure in a way that it works at a lower price point for larger numbers of companies in the long run.

Find this answer in Thilo Huellmann, CTO of Levity, on using no-code AI for workflow automation
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