Why AI and Machine Learning Have Cases Outside of Cloud

Cloud computing has been a key part of the rise of AI, but as more organizations look to improve their capabilities, they're moving toward a hybrid approach that includes both cloud and edge computing. Since cloud computing is often thought of as another term for all-in-one solutions, you might wonder why companies want to move away from it at all. The fact is, there are many reasons why customers are choosing hybrid approaches over purely cloud-based architectures.

AI Lurking in the Shadows

You might not know it, but AI has been in the cloud for a long time. Amazon, Microsoft, Google and other cloud providers have been offering machine learning services as a service (SaaS) for years. These services make it easy for developers to build applications that use machine learning models without having to worry about how those models are trained or implemented. For example, if you want to build an e-commerce site that recommends products based on customer behavior data and past purchases, you can do so quickly with Amazon’s machine learning APIs or Microsoft's host of AI services (AI Builder, AutoML).

With these tools, developers don’t need extensive knowledge of deep learning or neural networks; instead, they simply upload their input data set and then let the service take care of everything else—including training the model.

The Case for Living on the Edge

There are several reasons why you might be interested in moving your AI and machine learning to the edge

Perhaps you need to be close to the data. If you're using a cloud provider, there's no guarantee that they will have what you need on hand, or even within reach. The edge is where all of your data resides. It's also going to make it easier for you to work with this data directly as well as process it without having to rely on a third party for storage and processing purposes.

Given today's world of shared resources you need to be closer than ever before because latency is so important now. Latency refers not only how long it takes for information from one point (like your smartphone) gets transferred over the internet but also how quickly those insights can be integrated into other systems such as voice assistants or chatbots—and ultimately how fast these insights can be acted upon by consumers who may want an answer right away!

What Does This Mean for the Cloud?

The cloud is still considered by many to be the best way to implement large-scale machine learning applications. However, in some cases, it may not be the optimal choice. For example, if you want to make use of a specific model that isn't available on any public cloud service (such as an enterprise model or one built by yourself), it's possible that you will have better luck running it locally.

In other cases, using local servers may be more cost-effective than running your models in the cloud—particularly if your application requires significant amounts of storage or computational power.

It's clear that we have yet to find out what exactly this shift towards on-premise AI will look like; however, there are plenty of signs pointing towards a growing demand for local AI solutions over time.

Security Stays At The Top

Security is one of the main concerns for many customers. In fact, according to a survey conducted by Forrester, security is considered a top priority in every industry and among every user group.

A number of factors contribute to this concern:

• There’s no denying that cloud computing offers incredible benefits when it comes to scaling computation power and storage capacity. However, many people worry about handing over control of their data — or even just access to it — to third parties.

• It can be difficult to trust that sensitive information won't be accessed by unauthorized entities while it's being processed in the cloud, especially when using services based on proprietary technology.

• If you're using open-source tools like TensorFlow as part of your AI development process, there's even more reason for concern because these tools aren't always built with security as a top priority (as shown by some recent vulnerabilities).

The Cloud Is Not The Only Place For AI

As AI continues to spread through more industries, it will likely continue to be a hybrid affair.

To many people, AI means a single thing: machine learning. And while machine learning is an important part of what makes up AI, it's not the whole story—it's more like one of those toppings on your pizza that makes it great. Think about it this way: You can apply machine learning algorithms to any data set, but what does that look like? In order for an application or process to be considered "AI," there needs to be some kind of intelligent behavior involved in its implementation. Machine learning alone is just math; when you apply it correctly (that is, with contextual understanding), then you get something that looks like intelligence.

Cloud Computing Centralized.

Cloud computing has been an important part of AI and machine learning development, but it may not be the only piece of the puzzle.

Cloud computing has allowed companies to use their large datasets without having to invest in expensive hardware and software. This has helped them train their models much more quickly than before, which is essential if you want to make money from AI services that can be deployed into production at scale. As a result, many businesses are now using cloud services like AWS or Azure for training—but what happens next?

When you start using these systems, there are two things that could happen: either they get bigger, or they get better. What we've seen so far is that people who have started with pretty simple models have been able to scale up their computation power very easily (because it's usually just a matter of clicking "add more machines"). The second option would involve making changes such as adding new layers within your neural network architecture or changing how you deal with missing values during inference time. These changes will require adjusting your codebase significantly—and that’s where problems begin happening when working in the cloud environment alone.

Let's not forget to mention the centralization of cloud resources, while cloud computing provides scalability options, there are many instances of organizations having significant disruptions as they are too centralized on a single cloud provider.

Conclusion

The future of AI is still in development. It’s too soon to say that we’re going to see more companies moving their workloads to edge computing, but as more industries adopt this technology, you can bet that they will also be looking for ways to secure and protect their data. The cloud has been one of the main drivers behind machine learning and AI development over the last few years (and decades), but it may not be able to keep up as things get bigger.