Azure Stack Edge

Intelligent edge solutions, powered by AI

Enabling the intelligent edge

Edge computing enables organizations to leverage the cloud without latency, bandwidth or connectivity constraints that can impact business operations or impair the customer experience.

Neal Analytics builds intelligent edge solutions powered by Azure Stack Edge so you can leverage Microsoft Azure services, even when a direct, cloud-based option isn’t possible. This means virtually any solution, including AI-driven solutions, can function with or without internet connectivity, and enables organizations to process data locally, avoiding excessive time and bandwidth requirements to stream and process data in the cloud.

What is Azure Stack Edge?

Azure Stack Edge is a cloud-managed, hardware-as-a-service solution shipped by Microsoft. It brings the power of the Azure cloud to a local and ruggedized server that can be deployed virtually anywhere local AI and advanced computing tasks need to be performed.

Capable of running without a consistent internet signal, it can be used to process videos for local security systems and speech for call centers in real-time, or to run advanced machine learning models when it is not technically or economically viable to do so directly to the cloud.

This solution allows companies to deploy containers running Azure technologies directly onto the local server, decreasing latency of a round-trip to the cloud and reducing project risks caused by network issues.

What is Azure Stack Edge?

Azure Stack Edge brings the power of Azure cloud to a local and ruggedized server that can be deployed anywhere local AI and advanced computing tasks need to be performed.

It can be used process videos for local security systems and speech for call centers in real-time, or to run advanced machine learning models when it is not technically or economically viable to do so directly to the cloud.

This solution allows companies to deploy containers running Azure technologies directly onto the local server, decreasing latency of a round-trip to the cloud and reducing project risks caused by network issues.

Azure Stack Edge supports reliable AI

Reliable AI

When your path to using cloud AI services is barred by cloud latency, poor connection, bandwidth constraints and data privacy concerns, a popular option is to run these services at the edge. This enables organizations to keep services running and close to where the solution can help the business in a reliable way, regardless of outside network issues.

Azure Stack Edge runs containerized cloud services, meaning it only needs periodic connections with the cloud for maintenance, updates, and to transmit usage metrics. This enables organizations to rely on AI-driven cloud solutions even in environments where a consistent, reliable connection is impractical or otherwise difficult to achieve.

Azure Stack Edge lets you easily scale out AI

Scalable AI

Solutions built on Azure Stack Edge leverage the same AI support, containerized services, computing and storage capabilities that power the Azure platform. The boxes are seamlessly managed through the Azure portal, just like other Azure services and resources. Organizations can also deploy, manage, and received training on their AI and ML models implementations in the Azure portal..

Organizations can also scale local capabilities by adding additional Azure Stack Edge devices. This means that the simple, low-cost rental model of Azure Stack Edge makes it a flexible solution that is simple scale as business grows.

Optimized AI

Hardware acceleration capabilities provided by Azure Stack Edge enable organizations to optimize the server to efficiently run workloads. This means that organizations can leverage their Azure Stack Edge devices to capitalize on AI and ML models built by Neal Analytics, allowing for quick, more cost efficient workloads.

By combining our flexible engagement model, accelerators and expertise, Neal Analytics and our team of AI experts can quickly optimize your Azure Stack Edge solution to maximize results and minimize hardware costs, resulting in reduced Azure consumption rates and a quicker time to value.

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