Algorithmia was founded on the idea that lots of people who write algorithms, from academics to private sector employees, have good products but no way to commercialize them. It launched in 2014 as a marketplace for people who want to buy or sell algorithms, and raised a $2.4 million seed funding round.
Today, there’s more to the business, including a service launched in 2017 that helps developers manage and deploy artificial intelligence and machine learning algorithms in their applications (keep reading for more on Algorithmia’s early work in the area). Algorithmia now has a lot more money, too; it announced a $25 million Series B funding round this morning to use on product improvements, new hires, and international growth.
Seattle-based Algorithmia received the funding in a round led by Norwest Venture Partners, joined by Madrona, Gradient Ventures, Work-Bench, Osage University Partners, and Rakuten Ventures. (Madrona and Rakuten participated in the seed round, too.)
As the prevalence (and buzzworthyness) of machine learning algorithms has expanded in recent years, so has demand for Algorithmia’s offerings, the company says. Some 90,000 engineers and data scientists use its service, as do the United Nations, US intelligence agencies, and various private companies. In 2016, the company landed a deal with In-Q-Tel to provide a private algorithm-sharing platform for the US intelligence community.
At the same time as that deal, Algorithmia began hosting and distributing deep learning models trained on big data sets, as Xconomy reported in July 2016. It provides not only a storefront for algorithms, but also the cloud infrastructure on which to run them. The company has multiple models for compensating algorithm creators.
“Productionizing machine learning models manually was a serious challenge before we found Algorithmia,” Michael Fischer, chief of innovation at MS&AD, a company affiliated with Toyota and an Algorithmia customer, said in a news release. “The AI Layer gave us the tech stack to smoothly deploy and manage our machine learning lifecycle and their team goes above and beyond to ensure that our efforts are successful.”
