Algorithmia Blog - Deploying AI at scale

Challenges productionizing embedding engines

what is an embedding

As many applied ML practitioners know, productionizing ML tools can be deceptively difficult.

At Algorithmia we’re always striving to make our algorithms the best in class, and we’ve recently made a series of performance and UX changes to our Document Classifier algorithm, and put work towards generalizing it to other problem spaces outside of NLP. These changes were dramatic; we reduced our lookup time from O(n) to O(log(n)) and drastically improved the user experience by reducing unnecessary clutter, but it was far from easy.

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Algorithmia Engineering Team Spotlight: James

At Algorithmia we’re lucky enough to be surrounded by group of wildly intelligent, quirky, and fun engineers. We’d love for you to come by and meet them in person, but until then we’ll post a series of interviews introducing you to some of the talented people who are creating the future of AI.

Today, we’d like to introduce you to one of our remote employees, Canada James (not to be confused with Design James and DevOps James – both of which you’ll learn more about soon). James lives and works in Nova Scotia and has been part of the company for about two years as an algorithm engineer.

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Algorithmia Engineering Team Spotlight: Patrick

At Algorithmia we’re lucky enough to be surrounded by group of wildly intelligent, quirky, and fun engineers. We’d love for you to come by and meet them in person, but until then we’ll post a series of interviews introducing you to some of the talented people who are creating the future of AI.

Drum roll please…. (and not just because he’s lead bass in two local bands)…. Meet Patrick McQuighan! Patrick is one of our back end engineers and is working on solving the most complicated AI scalability problems in the industry.

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Algorithm Spotlight: Crowd Counter

Source: Wikipedia

Despite only making it into the political mainstream recently, crowd size estimation has always been an important task for corporate development, retail planning, and resource allocation. It helps property owners and event organizers predict demand, understand utilization of physical locations, and test different product launches and arrangements. And Machine Learning is making it more accessible than ever.

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