Inspired by real-world deployments of artificial intelligence (AI) and machine learning (Ml), Oracle recently released GraphPipe into open source—the main purpose of which was the simplification of the deployment and querying of machine learning models for developers, freeing them to choose from a variety of their preferred frameworks and tools. As simplification of AI and ML continues, service providers will more readily derive insight from the enormous amount of data that comes in from millions — and ultimately billions —of network elements, devices, monitors and sensors.
If you look at the leaders in the triumvirate of AI, ML and predictive analytics, the most popular applications today revolve around customer-service chatbots, speech and voice services, and more and more around predictive maintenance for cell towers and power lines (i.e., equipment troubleshooting, technician updates).
For example, Verizon’s investment in analytics and AI-driven technologies such as ML and chatbots have helped it proactively address hundreds of customer-impacting events related to speed and QoS of its FIOS service. AI-driven monitoring of services and predictive models are helping the company transform gigabytes of data from network elements and monitors into insight about potential issues and interruptions to customers before their customer experience is affected.
AT&T is another service provider that has focused on AI-driven voice apps and speech recognition technology for improving customer experience, as well as an impending strategy to build self-healing and self-learning networks. In its most recent move, AT&T began capitalized on FAA approval of drones, using AI and ML for analysis of drone-captured video for tech support and infrastructure maintenance of cell towers—something that could not only reduce costs, but prevent fatalities related to cell-tower maintenance.
As cognitive technologies continue to help CSPs automate operations, such as call center processes and outage troubleshooting, they will set the stage for a future of increasingly secure and self-healing networks — a crucial step toward 5G, where AI, ML and predictive analytics will help manage and orchestrate the rapid growth of traffic on mobile networks and IoT’s prodigious data volumes.
In that evolution, the interrelationships among AI, ML and analytics will become more important to CSP strategies around customer experience, digital business models and network evolution.
To find out more about the interrelationships of AI, ML priorities, download our recent white paper, “Artificial Intelligence and Machine Learning are the Future of Communications.”