The fund targets information technology and business products and services (B2B) sectors. The fund is located in Menlo Park, California. In part, Uppington noted, that’s driven by the fact that a lot of enterprises are now getting to the point in their AI journey where they want to put models into production and are starting to face these quality challenges. Harpoon Ventures is a VC investing group that backs technology-based companies. Harpoon Venture Fund III is a 2021 vintage venture capital fund managed by Harpoon. 60 minutes featuring the brightest minds on Wall Street, taking you. Harpoon Ventures Management, LLC operates as a venture capital.
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The company says it saw its revenue grow over 5x since it raised its Series A round in late 2020. Bloomberg The Open Jonathan Ferro drives you through the market moving events from around the world on Bloomberg's The Open. We think that we can help the world get to those better tools and more agile-like development with this kind of comprehensive, fast quality testing and making it really easy for the data scientists to use it.” About Harpoon is an early-stage venture capital that specializes in finance and investment management.
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That reduces the quality of your development process, just like it did in software development in the 90s.
“Data science is very waterfall and the models are still pretty black box. Harpoon Ventures is a VC investing group that backs technology-based companies. “We’re in the space where software development was in the 90s, before you had tools and agile development methodologies,” Uppington said. To do this, the company’s service can be integrated right into the kind of Jupyter notebooks that most data scientists are already using to build their models anyway, for example. TruEra argues that an enterprise AI quality management solution needs to approach these problems head-on, starting 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. And once a model can finally be put into production, businesses have to ensure that quality remains high, even as some of the underlying data changes.
Uppington argues that it’s not just hard to design and build high-quality models to begin with, but there’s also still a lot of concerns around trust, transparency and fairness when it comes to putting models into production - and increasing regulatory pressure around AI fairness is giving enterprises pause because they need to put auditable systems in place to conform to these rules.
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