Additionally, the need for teams to analyze this data and find ways to automate and predict issues before they occur is driving companies to increasingly depend on AI and machine learning. This large shift in computing will give rise to the need to streamline end-to-end customer processes from development to deployment at the edge, what we call EdgeOps.
The EdgeOps approach uses innovative edge-optimized platform to integrate AI and machine learning with an infinite set of distributed metadata generated by sensors, machine vision, deep learning, log files, and more, then leverages an edge-based systems infrastructure software platform to automate the deployment, management and maintenance of services for maximum performance at scale.
Adopting an EdgeOps Strategy Will Accelerate the Path to Digital Transformation by Automating AI/ML Across the Enterprise:
Increase service performance and reliability.
Increase uptime of mission critical applications.
Remediate intermittent bandwidth in real time for better Quality of Service (QoS).
An Effective EdgeOps Platform:
Leverages embedded intelligence and AI/ML to drive high performance across the organization
Runs real-time, latency sensitive analytics on high volumes, varieties and velocities of streaming sensor data at the edge
Provides synchronized view and coverage of all of your cloud-to-edge deployments
Seamless integration across microservices, no matter their location
Vastly improves asset performance across the enterprise
The Right Solution for
Edge-to-Cloud Deployments
We believe that leveraging AI to automate microservices across a cloud-to-edge deployment model will fundamentally change the way organizations gather and use data, react to information, make operating decisions based on embedded intelligence, and execute operating tasks. The Adapdix EdgeOps platform enables the data driven future with true embedded intelligence and adaptive AI.
