Run Your AI Inference at the Edge to Unlock Insights Faster

Businesses are advancing their AI strategies quickly, from accumulating knowledge to training models. Now that training models are more mature, vertical-specific and trusted, we expect companies to spend more time on AI inference, particularly at the edge.

Edge computing for AI inference enables real-time decision-making for use cases across many industries. Processing data locally, in proximity to data sources, reduces latency, ensures compliance and allows organizations to react immediately to AI inference insights. This is crucial for sectors such as healthcare, transportation and manufacturing, where milliseconds matter.

Running AI inference at the edge also increases overall efficiency, enhances data privacy and security, strengthens reliability, improves user experience and enables predictable costs.

Deploying AI in centralized and distributed environments

Where you deploy AI depends on where you’re at in the application cycle. There are multiple considerations for determining the right environment, including latency requirements, primary user locations, where data is generated and cost. Choosing a hybrid approach of centralized and distributed environments may be the logical option. It’s about putting the right workloads in the right places.

If you’re training or refining AI models, you’ll typically need centralized clusters of substantial compute power, storage for volumes of data and advanced AI cooling resources. These centralized resources could be in the cloud, on-premises or in colocation data centers.

Companies can use a distributed environment with smaller clusters to run the models and infer the data. These environments can be found at the edge, where you may have high concentrations of users and devices. The edge can be located anywhere.

While it’s true that AI model training has traditionally happened in centralized environments, this is beginning to change. Companies increasingly see the value of training models in more places. Data collected at the edge can be used to retrain AI models locally, creating a constant feedback loop over time.

Where you train AI models should depend on your requirements. If you have data privacy concerns, such as using proprietary data to train your AI models, an on-premises data center or a high-performance colocation data center can provide the control and security capabilities you need to address these concerns.  You may be able to find the CPU and GPU hardware you need in colocation data centers in your preferred edge locations. You’ll get the best of both worlds in these environments; you can train AI models and run inference in a single location.

How does edge computing enhance distributed AI inference?

Integrating edge computing into your IT infrastructure strategy allows you to move away from centralized on-premises IT. Instead, you can place workloads like AI inference where you need them most. Distributing AI inference across edge data centers provides benefits that centralized compute environments can’t:

Lower latency: Processing data closer to users and devices is essential for use cases with stringent latency requirements, such as autonomous vehicles or industrial robotics. Other latency-sensitive applications and services, such as voice assistants, predictive maintenance and environmental monitoring, can also benefit from localized data processing.

Reduced data backhaul: Running AI inference at the edge reduces the amount of data that gets transferred to centralized compute environments. Businesses only need to transfer the relevant data insights and results rather than the complete raw datasets. This helps improve bandwidth efficiency and keep networking costs predictable.

Enhanced privacy and security: Storing and processing sensitive data at the edge minimizes the need to transmit it back to the cloud or other locations via the internet. This is especially crucial for data privacy compliance in the financial services and healthcare sectors. In other industries like manufacturing, running AI inference on edge systems allows companies to operate with closed systems, and thus avoid connecting with the internet. When necessary, they distribute training models over private networks.

Improved reliability: Operating edge AI systems and devices independently, without internet connectivity to cloud and centralized systems, is crucial for industries such as healthcare, manufacturing and transportation. In these sectors, downtime can be highly disruptive, even life-threatening. Applications and devices must continue analyzing data, identifying inefficiencies and making automatic adjustments in real time, even if there’s an interruption to network connectivity. Deploying at the edge makes this possible.

Resource consolidation: For retailers or restaurants operating multiple stores within the same city, it may make sense to consolidate their compute resources at a single location known as the metro edge. From the metro edge, they can ensure connectivity to end devices with less than 10 milliseconds (ms) of latency. Taking this approach helps businesses meet local data sovereignty requirements and lower costs.

Edge computing and AI inference use cases

Companies in multiple industries are using edge computing for AI inference to advance their AI agendas. Here’s how two companies achieved significant results with their AI inference deployments at the edge:

• Rideshare company PickMe wanted to enhance security and improve operational efficiency. They integrated AI into their app to enable real-time fraud detection. Then, PickMe leveraged high-bandwidth and low-latency interconnection on Platform Equinix® to connect with their MLOps platform and create multiple AI inference edge locations that are less than 10ms RTT from end users. They used this data to produce heatmaps of supply and demand, which helped them match riders with drivers more efficiently.
• Healthcare technology company Harrison.ai wanted to develop customized AI-enabled tools to help clinicians make faster, more accurate diagnoses when analyzing chest X-rays and brain scans. They deployed NVIDIA DGX A100 systems on Platform Equinix for data analytics, scientific computing and AI development. Harrison.ai reduced model training time and sped up the ML development process while protecting confidential patient data.

Deploy AI inference at the edge with Equinix

Choosing the right partner for this next step in your AI journey is crucial. A colocation data center provider with a global footprint can help you deploy centralized and distributed compute environments where you need them.

Platform Equinix includes a global network of data centers in 73 key markets in 34 countries. In addition to providing traditional colocation, we offer the flexibility of digital infrastructure, private connectivity solutions and cloud on-ramps to all the major providers.

Equinix Fabric®, our software-defined interconnection solution, provides secure, private, high-performance connectivity for data transfers across enterprises, clouds, networks and IT services without traversing the internet.

Our robust ecosystem of more than 10,000 businesses includes leading cloud providers, networks and IT services providers, and AI companies–all interconnected for seamless integration with your company.

To learn more about how our customer, PickMe, used AI inference at the edge, read their success story today.