Algorithmia Blog - Deploying AI at scale

Going Mainstream: Integrating Machine Learning in the Cloud

Adding machine learning to the cloud

You might have heard: Google unveiled their new machine learning platform to “pour ML all over the cloud.” The moves makes TensorFlow available to developers to do machine learning in the cloud with their own data.

Machine learning is developing fast, and “what will distinguish the good companies from the rest are things like domain expertise, quality of the dataset, and the ability to find the right problems to solve.” The Economist adds, “the firms that develop an early edge in artificial intelligence may reap the greatest rewards and erect barriers to entry.”

“At its highest level, machine learning is about understanding the world through data,” said Geoffrey Gordon, acting chair of Carnegie Mellon University’s Machine Learning Department. “Anything you can think of — public policy, finance, automobiles and robotics, for example — there’s a role for machine learning.”

While algorithm development is still broken, Coursera’s new Data Science Masters program is a step in the right direction. And, here are the top machine learning books for data scientists and machine learning engineers.

Confused about ML? Here’s how to approach machine learning as a non-technical person.

Product manager at Algorithmia helping to give developers super powers.

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