All posts by Diego Oppenheimer

Algorithmia Raises Series A Funding

Algorithmia Series A

To our users,

Today we are excited to announce that Algorithmia has completed a Series A financing of $10.5M led by Google’s new fund focused on AI and machine learning investments with the participation of Madrona Venture Group, Rakuten Ventures, Osage University Partners, and Work-Bench.

Financing rounds are rarely a goal state (or at least they shouldn’t be) but an important milestone in allowing a company to continue to build out its mission.

We intend to focus this coming period on two areas:

  • Marketplace: through, we have reached more than 45,000 developers and 3,500 algorithms, functions and models. We are expanding our efforts in accumulating the best algorithmic knowledge out there, expanding our network of universities, and becoming the hub to discover and access the latest algorithms, functions, and microservices.
  • CODEX: our private cloud algorithm-as-a-service solution has a growing customer base within Fortune 1000 and federal government, and we are excited to help them build up their algorithmic moat, help them discover and integrate their internal algorithms, functions and models and productionize at petascale. All of this while providing the necessary security and audit controls required by the most regulated industries.

Our mission has been and remains to “make state of the art algorithms discoverable and accessible to everyone” and we are thrilled to see the excitement behind that goal.

We also would like to take this time to thank every one of our users for checking us out, trusting us with your applications and allowing our company to reach today’s milestone. We know we would have never done it without you and for that, we remain eternally grateful.

Our team is expanding – join us! check out our openings at

Humbly yours,
Diego Oppenheimer
CEO – Algorithmia

Machine Learning with Humans in the Loop

TL;DR The most accurate machine learning systems to date are those that use a “human-in-the-loop” computing paradigm.

Though we have seen huge advances in the quality and accuracy of pure machine-driven systems, they tend to fall short of acceptable accuracy rates. The combination of machine-driven classification enhanced by human correction, on the other hand, provides a clear path forward in acceptable accuracy. Below we will describe a real-world use case of building and scaling these type of systems.

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Making University Research Discoverable and Accessible

Distribution, Reach, and Monetization of University Research and AlgorithmsOne of the most rewarding parts of working at Algorithmia is that we get to collaborate with amazing university researchers across the globe.

Last May, Richard Zhang, Philip Isola, and Alexei A. Efros from the University of Berkeley Vision Lab published their work “Colorful Image Colorization.” This paper describes a novel use of a convolutional neural net (learn more about deep learning) for colorizing black and white pictures.

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