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DevOps for AI Apps (Part 1)

Looking through various recent studies on the success of AI Apps for enterprises, It seems many businesses have developed AI Apps, but the apps only very rarely make it into production. Challenges seem to come from everywhere, from infrastructure and capacity, to skills scarcity, to the lack of automation or virtualization, where data scientists are having to request physical GPUs manually!

Over the last decade in the Cloud and Automation space, we’ve already been through a lot of this pain. Now is the opportunity to apply the many lessons we’ve learned, to make AI Apps even more accessible to enterprises.

Specifically, DevOps for AI Apps could be a great start.

Smarter apps, better results.

Software is the key to innovation and technological evolution in today’s world. It’s no surprise then, that the software being written today is often leveraging artificial intelligence to take things to the next level. For the purpose of this article, we should consider an ‘AI App’ to be any application or IT service, which leverages machine learning algorithms to improve accuracy and efficiency.

If any of these terms are new to you, check out AI/ML Demystified to get up to speed.

AI apps are becoming commonplace, whether it’s Siri on your iPhone, Netflix deciding what you should watch, fraud detection from your bank or even supply chain forecasting from someone like Amazon who use the intelligence to better predict purchasing habits.

These are the next generation of applications and they are already all around us. 

The idea of leveraging something like machine learning to level-up your next enterprise app project could be a good one for many reasons, but building one and getting it to market might not be quite as straightforward as its traditional counterpart might be.

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