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.
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.
Machine Learning is about rapid experimentation and iteration, and without keeping track of your modeling history you won’t be able to learn much. Versioning let’s you keep track of all of your models, how well they’ve done, and what hyperparameters you used to get there. This post will walk through why versioning is important, tools to get it done with, and how to version your models that go into production.
Machine Learning is emerging as a serious technology just as mobile is becoming the default method of consumption, and that’s leading to some interesting possibilities. Smartphones are packing more power by the year, and some are even overtaking desktop computers in speed and reliability. That means that a lot of the Machine Learning workloads that we think of as requiring specialized, high priced hardware will soon be doable on mobile devices. This post will outline this shift and how Machine Learning can work with the new paradigm.
Multi-cloud is quickly becoming the de facto strategy for large companies looking to diversify their IT efforts. At Algorithmia, we deploy across multiple clouds and recommend it for Machine Learning pipelines and portfolios. This post will outline the pros and cons of a multi-cloud architecture, as well as its applicability to Machine Learning workloads.
The first thing to understand about this emerging strategy is that it’s very popular. Almost 80% of enterprises that utilize public clouds use two or more of them, and 44% of those enterprises use 3 or more. Overall, 61% of all enterprises surveyed here are using two or more cloud providers.