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AI at the edge: Distributing data center capability

By Max Burkhalter
June 15, 2021

AI is coming into its own, and is currently poised to transform virtually every industry. Business leaders are starting to realize how critical it is to implement AI and stay competitive in a rapidly changing landscape. According to SEM Rush, AI adoption is at the forefront of many executives' minds.

  • 80% of retail executives expect their retail companies to adopt AI-powered intelligent automation by 2027.
  • 75% of executives fear going out of business within five years if they don't scale AI.

However, computing power used in building and training AI models has increased exponentially. How can companies stay level with the need for AI adoption and afford to scale the processing power needed? The answer is the edge. 

AI at the edge
In a typical AI workflow, massive datasets are collated, cleaned, sorted and microsegmented for modeling. Training models can be used for prediction or inference, and fine-tuned iteratively. This takes incredible amounts of bandwidth and processing power, and most on-premise servers won't be able to keep up with demand. Renting rackspace in the cloud to store and crunch data is becoming a go-to (public clouds have traditionally been an attractive place to deploy AI for auto-scaling), but an increasing number of use cases require distributed AI deployment at the edge.

This means meeting increasingly stringent requirements. Strategically running AI training workloads at the edge can mean new challenges to face in regard to power and performance as well as data privacy, security and gravity. Aggregation must also be considered. Inference at the edge means another layer of complexity, including latency, availability and device resources. Simplifying and automating as many processes as possible can reduce errors.

The accelerated pace of AI deployments for training and inference are being matched by enhanced as-a-service capabilities, as new options for infrastructure deployment automation and orchestration are made possible by hybrid multicloud AI environments and edge computing.

Grid-positive data centers
Computing and processing power depends on actual power, and data servers currently consume a whopping 1% of all power worldwide according to Data Center Knowledge. Demands are being made for such centers to reduce energy consumption and corresponding carbon emissions by implementing design innovations and energy-efficiency measures. Many operators are now committing to 100% renewable energy and carbon neutrality, in the face of an increasingly digital economy with ever-expanding datasets. 

The increased focus on sustainability naturally supports open data center infrastructure standards, and a shift to decentralize. Moving AI to the edge can help defray demands on data centers and reduce the impact of data transmission and number crunching since much can be done at the edge to facilitate quicker data handing and discarding of unnecessary data. Add to this the upcoming restrictions to protect personal data and data processing at the edge becomes even more of a priority.

Diginomica notes that we are approaching a cookieless future, changing how data is collected, stored and accessed, Distribution of data and access from devices instead of browser cookies necessarily supports AI at the edge computing, and drives computing and network resources closer to the edge. This can be expected to usher in a new generation of grid-positive data center projects. Individual businesses must also reimagine their data stacks, enabling end-to-end orchestration at software speed, and leveraging cloud-native technologies to keep AI function at the edge and beyond.

Perle helps you with physical infrastructure while you drive the future of AI forward to the edge. Read our user's success stories to learn more.


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