Cloud AI Is Hot, But Here’s Why You Should Think Twice

From IT Pro Today

Want to build artificial intelligence (AI) into your app, but don’t want to have to collect your own data or train your own models? The cloud has a solution for you.

Cloud-based AI services have emerged as one of the hottest service offerings from the major public clouds, which are eagerly inviting developers to make use of them. But while there are good reasons to use cloud AI in some cases, there are also some arguments to be made for avoiding it. In fact, there are so many reasons why AI in the cloud doesn’t seem as beneficial as other types of cloud-based services that I sometimes suspect cloud AI will turn out to be a fad, or at least prove less popular than the cloud providers hope. Here’s why.

What is cloud AI? When I talk about cloud-based AI, I mean any type of cloud-based service that is designed to help developers integrate AI or machine learning into their applications.

Today, all of the major public cloud providers offer a suite of AI services and resources that developers can use to do everything from building speech and image recognition into their apps, to training custom machine-learning models, to parsing large data sets. (You might say that the latter functionality is really a form of data analytics, not AI, but the cloud providers are increasingly pitching it as an AI solution.)

Why use cloud AI? Generally speaking, cloud AI offers the same types of advantages as other cloud-based services.

It eliminates the need to set up and manage your own infrastructure for hosting AI applications. It gives you instant access to pre-baked configurations, models and AI-relevant data curated by someone else, which is especially beneficial if your team lacks in-house AI expertise. You pay for it on a monthly basis instead of having to make large upfront investments, as you would if you built your own AI infrastructure from scratch.

You could say the same things about the benefits of most services that run in the cloud, as compared to their on-premises alternatives.

The shortcomings of cloud AI. Cloud AI is also subject to many of the same drawbacks as most cloud services, like additional security challenges and potentially higher total cost of ownership; yet, cloud AI also has some drawbacks that you don’t face with most other types of cloud services, which is why I suspect cloud AI might turn out to be less of a big deal than some folks imagine.

Here’s a rundown of the top challenges of cloud AI:

1. Data privacy. AI depends on data — lots of data; thus, when you do AI in the cloud, you need to move lots of data into the cloud. Generally speaking, storing data in the cloud is not inherently problematic. It poses some additional security challenges, but they are usually easy enough to solve. However, when it comes to AI applications, the data you need to upload to the cloud and pass through a cloud vendor’s AI engine may be particularly sensitive. For example, if you are using a cloud-based image- or voice-recognition service to upload pictures or voice recordings of customers and identify individuals based on that data, you get into some especially sticky territory regarding privacy and compliance. This is not to say that you can’t safely upload data into the cloud for AI purposes, even if the data is highly sensitive. But it does make things extra complicated.
2. Speed and performance. AI’s data-hungry nature poses a second challenge for cloud-based AI applications: Moving large volumes of data over the network takes a long time, and there is not much that cloud vendors or end users can do about it. Edge computing is a partial solution, but it’s not always …
   
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