Beyond clouds: Why your AI strategy needs an edge perspective

We’ve entered the intelligent age, in which vast quantities of data are being put to work with AI to improve products, boost efficiency, enhance healthcare and transportation, and provide more personalized experiences. Emerging AI use cases like autonomous vehicles, healthcare monitoring, smart manufacturing, and precision farming promise to make our lives and our world better.

As GPUs become more affordable and accessible, AI is likely to become ubiquitous, with businesses of all sizes taking advantage. But to achieve all the benefits of AI-driven insights and efficiencies, companies need the right IT infrastructure in the right places. For cutting-edge AI use cases, real-time data processing at low latency is a must. And that requires processing power in edge locations, close to where the data is generated and used.

IT models that rely too heavily on public cloud simply can’t meet the real-time processing requirements of many AI applications. Moving data to far-off public cloud data centers not only increases latency, but it can also introduce data privacy and security concerns. To succeed with AI, companies need interconnected hubs in edge locations to complement their public cloud use. A robust hybrid multicloud infrastructure that’s well connected to global ecosystems in edge locations will make businesses unstoppable in the AI-powered future.

Data is forever, but who owns it is not

Data is now being gathered at an unprecedented scale. Every day, hundreds of millions of terabytes are created worldwide. AI and machine learning are already demonstrating how rich with possibilities that data is.

At the same time, organizations are facing numerous data-related risks:

• Data breaches
• Changes of ownership through mergers, acquisitions, bankruptcy, and private equity takeovers
• A growing number of data regulations globally
• Proprietary and sensitive data that needs to be kept private

Companies are therefore scrutinizing how they can best protect their most valuable resource. In industries like healthcare and life sciences, financial services, and government and defense, highly sensitive data underpins organizational research, development, and competitive advantage. It’s therefore crucial to process data locally in a secure environment where companies have control of data and can minimize exposure to external threats.

If data is forever, and it’s the differentiator for your business, the maintenance, privacy, security, and provenance of that data are essential. And that may mean rethinking where you put it and how you transport it.

The problem with going all in on cloud

For a time, companies were laser-focused on cloud adoption. Enterprises seemed to be racing to move as many workloads to the cloud as possible. And it made sense at the time: Businesses were trying to reduce risk by increasing the flexibility and scalability of their IT infrastructure. They wanted to break free from 60-month cycles of buying gear and get access to things like high-performance computing.

Over time, many have learned that going all in on cloud didn’t deliver the cost savings and risk reduction they’d hoped for. Even companies that were born on cloud have begun to realize that as they mature, they need greater control of their data. Many are undergoing cloud rebalancing initiatives.

In the era of AI, organizations are now working with so much data and need so much real-time processing that a cloud-first approach to IT is no longer the best strategy.

New AI use cases and opportunities require edge

The world is in an interesting historical moment with AI. We’re in the early stages of what’s sure to be a transformative technology. The internet was groundbreaking, but it took decades to get from the initial idea of hypertext in the 1960s to HTTP and the World Wide Web in the 1990s. The world of AI is evolving very quickly, but we’re still in the beginning of what’s likely to be a renaissance in how we live and work.

New AI models are being trained all the time, and they’re operating in all kinds of places: in smartphones, cars, wearable devices, self-checkout kiosks. IoT sensors are collecting data everywhere, and 5G networks are expanding to transport it. As an industry, we’re all still trying to figure out the best use of the technology, with clever and novel ideas landing in production every day.

Companies already recognize AI’s potential to help them solve their problems:

• In healthcare, remote patient monitoring and AI-powered diagnostics are improving patient outcomes.
• In manufacturing, digital twins and supply chain management solutions are transforming factory operations.
• In transportation, self-driving vehicles and fleet management solutions are improving safety and efficiency.
• In smart cities initiatives, traffic management and public safety services are reducing congestion and accelerating emergency response.

As more AI wins accumulate, companies will see that machine learning is the only way forward. But these AI applications require real-time data processing close to where data is generated and used. For data-driven decisions, companies can’t afford to transport data—even at the speed of light—to a far-off cloud or on-premises data center. They need infrastructure at the edge.

AI requires an infrastructure rethink

To meaningfully and securely reduce latency to support the response times of many AI use cases, organizations are rethinking their infrastructure strategy to include a mix of cloud and edge.

There are multiple ways for companies to meet their edge computing needs, using cloud availability zones, company-owned sites and branch offices, or colocation facilities. Regardless of how they do it, they’ll still need access to public clouds and a robust network that can securely connect all their resources.

In fact, the network is crucial for AI, because it doesn’t matter how sophisticated your AI equipment is if you’re not plugged into a good network. If you have sensitive or highly regulated data, it’s even more important that you have private connectivity options.

At Equinix, organizations can connect their private infrastructure with all the leading clouds, network service providers, and other IT services using any connectivity option they want, whether that’s public or private, physical or virtual. With 260+ data centers in 74 markets, we can help you find your edge, wherever in the world it might be. And you can continue to have that cloudlike infrastructure flexibility, so that you can move workloads organically, replicate deployments, and follow user groups to new locations. Equinix enables organizations to assemble their preferred mix-and-match set of private and partner-operated gear and stitch it all together on our network with clouds, SaaS providers, and anything else that’s part of their AI ecosystem at the edge.

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