Cutting-Edge Cloud Migration and AI Infrastructure Spikes Efficiency

About the Client

A communication & infrastructure company is based in the United States and specializes in building infrastructure for edge computing. It helps businesses run cloud-native applications closer to users by deploying compute resources at the edge of the network. The platform is designed to reduce latency and improve performance for industries like retail, telecom, and smart cities.

‍

The Challenge

The company aimed to deploy their new “Kinetic Edge” architecture to support Kubernetes workloads close to retail locations. They needed a system integrator to architect and implement a solution using Google Cloud Anthos and machine learning (ML). The solution had to be performance-tested across multiple architectural configurations.

The Solution

Anthos Bare Metal was deployed on the client’s Kinetic Edge hardware, establishing secure network connectivity between retail stores and Google Cloud Platform. Using Terraform, a data and monitoring pipeline was created to enable multi-store analytics. Additionally, an AI-powered Vision micro-services architecture was integrated with an object detection system to enhance operational efficiency, scalability, and performance.

The Result

Efficiency Boost: Validation enhanced operational efficiency, ensuring seamless failover and minimizing downtime risks.

Risk Mitigation: Benchmarking reduced risks by ensuring optimal performance and reliability across data centers.

Competitive Edge: Improved capabilities took the company ahead, attracting clients seeking robust infrastructure.

Cost Savings: Streamlined operations and enhanced reliability led to reduced operational costs and revenue loss.