TruEra, a startup that offers an artificial intelligence quality management solution, today announced that it has raised a $25 million Series B round. Existing investors Greylock Partners, Wing Venture Capital, Harpoon Venture, Conversion Capital, the Data Community Fund, as well as new investors Forgepoint Capital and the B Capital Group participated in this round. TruEra has raised over forty million dollars.
TruEra CEO and co-founder Will Uppington said that the quality challenge is the next big challenge in the field of artificial intelligence. We think that the main issue is preventing artificial intelligence systems from getting into real-world use and actually delivering.
TruEra is an image.
It's not hard to design and build high-quality models to begin with, but there are still a lot of concerns around trust, transparency and fairness when it comes to putting models into production. Once a model is put into production, businesses have to make sure that the quality remains high.
TruEra believes that an enterprise quality management solution needs to start with tools that developers can use while they train the model so that they can test and evaluate their models long before they go into production. The company's service can be integrated into the kind of Jupyter notebooks that most data scientists are already using to build their models, for example.
The space where software development was in the 90s was where tools and Agile development methodologies were used. That reduces the quality of your development process, just like it did in the 90s.
The company raised its Series A round in late 2020. In part, Uppington noted that a lot of enterprises are getting to the point where they want to put models into production and are starting to face these quality challenges. It's a good time to be in the quality space because of the regulatory environment and some high-profile failures.
TruEra approaches the problem through the lens of the model, with co-founder and Chief Scientist Anupam Datta having done some of the early academic work on artificial intelligence.
With more data available than ever, are companies making smarter decisions?
What would I want to do with machine learning data? I looked at all the companies and I was looking for the one that provided the depth and approached the problem through the lens of the model, as opposed to just the opposite direction, which I think is completely wrong. I want to see some proprietary research that creates distance from the competitors.
Taking open source libraries and putting a user interface on top of them doesn't cut it in this market.
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