All posts by Diego Oppenheimer

Announcement: Today we celebrate an important milestone.

series b announcement

We are extremely thankful for our customers and their trust in us, and we want to share this news with them first and foremost. This funding enables us to double-down on developing the infrastructure to scale and accelerate their machine learning efforts. 

We are also thrilled to welcome Norwest Venture Partners (NVP) to Algorithmia and are honored for the continued support of Madrona Venture Group, Gradient Ventures, Work-Bench, Osage University Partners, and Rakuten Capital. Rama Sekhar, a partner at NVP, will be joining Algorithmia’s Board of Directors.

Their support means two things:

    1. Our customers have communicated to our investors that they are getting the personalized support they need to manage their machine learning life cycles. They feel confident not only in the product decisions we’ve made to date but also in the delivery of future product features.
    2. This funding allows us to continue maintaining our market leadership position.

We have proven as a team that we can enable humanitarian efforts of the United Nations, individual developers, and the largest companies in the world to adopt machine learning.

More importantly, our customers have given us the opportunity to truly deliver on our potential—and for that, we are immensely thankful. If you use Algorithmia, we are especially thankful for your support.

We are laser-focused on our customers

As we pioneer the field of machine learning operationalization, we know that our users are also pioneering their fields—we empathize with the challenges they face and are laser-focused on their success.

Our customers choose us because we listen to and build features they need to solve real problems and we offer responsive onboarding and support. Our customers are key to our success—we will now push even harder to help, listen to, and learn from them.

Moving forward, we will accelerate our engineering efforts and maintain our lead as the best platform for managing the machine learning life cycle.

Finally, funding is never a goal—it’s simply fuel to ensure we deliver on our mission to provide everyone the tools they need to use machine learning at any scale. If the purpose of tools is to extend human potential, then machine learning is poised to become one of the most powerful tools ever created. The AI Layer is a tool that makes machine learning available to everyone.  

Feeling thankful and energized,

Diego Oppenheimer
CEO, Algorithmia

P.S. A small ask—please share the news with your network on LinkedIn to help us spread the word.

DevOps for AI – The AI Layer

When Google’s Gradient Ventures invested in us, they did so with an understanding that it is incredibly hard to deploy AI/ML infrastructure — and that every dev team is going to need to solve this problem.
Our solution, the AI Layer, is the best-in-class architecture.As our co-founder, Kenny Daniel says: Tensorflow is open-source, but scaling it is not. 
DevOps for AI presents massive challenges to all sizes of organizations. If your team is only a couple of developers, you don’t want to distract them from their primary mission by requiring them to put in a ton of effort supporting infrastructure.For large organizations, AI/ML models require a completely customized DevOps stack. Let us show you how…

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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. Read More…

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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