Cloud Edge
Machine Learning
Explore use cases including, LLM/GenAI, smart cities, computer vision, AR/VR for predictive maintenance, and AI assisted workload relocation with Aarna Edge Services (AES) — the number one zero-touch orchestrator delivered as a service.
What is Cloud Edge Machine Learning?
Edge computing and Artificial Intelligence / Machine Learning (AI/ML) are complementary technologies being leveraged together to glean new insights and create new possibilities at the network edge. Distributed AI/ML applications are now being deployed in multiple locations based on business parameters such as performance, privacy, and cost. Cloud edge machine learning involves deploying machine learning algorithms directly on edge locations adjacent to the cloud.
The flexibility inherent in cloud edge computing — combined with the capability, high-bandwidth connectivity, and low latency of 5G networks — now makes it possible to host AI/ML applications with cloud adjacent storage close to where the data is generated. This enables enterprise services with strict performance requirements and use cases requiring data aggregated from multiple sources. This includes use cases such as smart cities, computer vision, AR/VR for predictive maintenance and AI assisted workload relocation.
Enjoy the convenience of the cloud while maintaining data proximity
Explore the convergence of AI/ML, cloud, and edge computing, and the benefits of running machine learning workloads at the cloud edge with Aarna Edge Services (AES) — the number one zero-touch orchestrator delivered as a service.
AI/ML at the Cloud Edge
AI/ML applications today, such as for large language models (LLM), are mostly run on-prem or in the public cloud. Both approaches have pros and cons. But edge, cloud, and AI/ML have converged to a point where now there is a third way – applying machine learning at the cloud edge. Benefits of this approach include:
- Ability to process data close to where it gets produced
- Ease of use features at par with the public cloud
- OPEX savings
- On-demand usage
Computer Vision
Computer vision can generate large amounts of data. With hundreds or thousands of cameras being deployed, the traffic can easily add up to multiple gigabits. Moving this amount of data to the public cloud for computer vision ML processing can be quite expensive. An alternative is to run ML processing at the cloud edge, i.e., the colocation or datacenter location where the last mile access network terminates.
Generative AI
Powered by large language models (LLM), Generative AI programs like ChatGPT are revolutionizing the way we live and work. Cloud edge in a private cloud is an ideal place to collect data and run AI/ML algorithms for business intelligence. When using open source models such as Llama or Dolly, the user can have full control over the LLM model meaning there’s zero probability of data leakage into the public domain.
Given that the cloud edge can be easily connected to a company’s private data with a dedicated link to their datacenter cage or through SD-WAN breakout (see figure below), a cloud edge LLM will have unrestricted access to sensitive data for training purposes than an LLM running in a public cloud.
The above figure shows a Cloud Edge ML implementation with connectivity to a company’s on-prem locations over SD-WAN. The ML workloads could be LLMs like Llama or Dolly or computer vision ones such as NVidia Metropolis.
RAN-in-the-Cloud
One such edge location for AI/ML processing is the Radio Access Network (RAN). Ideally, a 5G radio access network would be hosted as a service in multi-tenant cloud infrastructure running as a containerized solution alongside other applications. This concept of RAN-in-the-Cloud allows RAN components (CU/DU) to be dynamically allocated, increasing utilization for better sustainability, and using spare capacity in off-peak hours to run AI/ML applications.
Aarna Edge Services (AES)
Aarna Edge Services (AES), is the number one zero-touch edge multicloud orchestrator delivered as a service. It features an easy-to-use GUI that can slash weeks of orchestration work into less than an hour. In case of a failure, AES includes fault isolation and roll-back capabilities. Support includes:
- Equinix Metal Servers with GPUs
- Equinix Fabric & Network Edge with Azure Express Route/AWS Direct Connect
- Pure Storage
- ML workloads
- NVidia Fleet Command + Metropolis, OR
- Open source Llama LLM, OR
- Open source Dolly LLM
Set Up a Cloud Edge LLM
Aarna Networks, Predera, and NetFoundry have partnered to offer a Private, Zero-Trust, Fully Managed LLM for to help you explore the world of generative AI. Choose from a variety of foundational models that you can fine tune with your corporate data to discover new insights and revenue generating opportunities. See this Solution Document to learn more.
Or, request a free consultation to learn more about how to apply these approaches to your business requirements and cloud/edge machine learning strategies or request a Free Trial of AES today.
