Edge Ai Solution

Edge Ai Opportunities and Challenges

By 2035 there will be one trillion Edge devices, requiring millions of Edge servers from a broad variety of hardware vendors. None of the typical conveniences are available at the Edge – no high-speed file systems, load balancers or GPUs – so delivering low latency Ai insights is challenging. Zeblok's Ai-MicroCloud^® is a software-defined Ai Ecosystem, which solves the problem of scaling at the Edge and makes it dead easy.

The Edge is an ecosystem play. Edge Ai challenges are thus the challenges to establishing and maintaining such an ecosystem, which fall into three categories:

Managing the Ecosystem

Edge Ai requires a diverse set of independent Ai software vendors (ISVs) and Ai algorithms. While ISVs may have built their applications in AWS or Azure, delivering to the Edge on different hardware and with different cloud providers is an expensive bespoke process for each Edge location.

Lowering the cost per Insight

There is a model-to-API gap and price-to-performance gap for inference engines.

Automation & Scale

Manageability, scalability, security, and compliance requirements in delivering from Cloud to Edge, on disparate hardware at Edge data centers is a software distribution and governance problem.

What Edge Ai Needs

Flexible

Architecture that supports different topologies

Scale

Rapidly deploy Ai to infrastructure across many locations

Curate

Broad choice of Algorithms ( NLP, V2X, CV )

Simple

Easy to deploy Ai inferences to thousands of edge locations

Support

High-Performance Computing (HPC)

Optimize

Better resource utilisation and cost Reduction

Manage

Software-defined Ai-Ecosystem

Delivering a digital foundation for edge Ai

As businesses in every industry embrace innovation in Ai and adapt to a changing technological landscape at the Edge, Zeblok's Ai-MicroCloud^® provides the digital foundation that helps them meet new demands and prepare for the future.

Zeblok's Ai-MicroCloud^® Edge Ai solution brings together all the core components & technologies to provide enterprises an End-to-End lifecycle management to create and manage an Ai ecosystem.

From designing flexible architectures that support different topologies, automating deployments to thousands of geographically dispersed Edge locations to providing an Ai-Platform-as-a-Service that enables to securely operate, monitor, manage and support an Edge Ai ecosystem, Zeblok's Ai-MicroCloud^® enables enterprises to be at the forefront of Digital Transformation 3.0, driven by the proliferation of Ai-APIs.

Technical Challenges

Orchestration

According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization.

Insight Quality

Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities.

Optimization

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers.

Heterogenous Architecture

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform.