Heavy Reading Analyst: Autonomous Network Operations Demand Agility & Adaptability

Kelsey Kusterer Ziser
4/4/2018
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Use case for machine learning
The use cases for ML are endless, from customer-facing opportunities such as chatbots, contact center optimization, fraud detection and prevention to networking use cases including security, prediction of network faults, and SLA monitoring and enforcement.

"There are lots of applications [for machine learning] in operations, so fraud detection for example, security, eventually service assurance," says Crawshaw.

Developing machine learning code is only one small part of building ML systems, says Crawshaw -- service providers still need IT infrastructure, process management tools, data collection tools and process management systems in place. Several standards bodies are working on addressing these challenges to applying ML to network automation. ETSI, for example, established the Experiential Network Intelligence working group, which is striving to create a system "that will sit alongside existing systems like the management and orchestration system for NFV," collect data and deliver suggestions back to the system, says Crawshaw.

The Telecom Infra Project (TIP) also has an ML working group focusing on machine-learning based network operations, customer behavior driven service optimization and Multi-Vendor ML-AI Data Exchange Formats to develop generalized ML models applicable across the telecom industry.

Telcos still have quite a few barriers to fully realizing the benefits of machine learning, Crawshaw explains: "Network engineers don't necessarily have all the mathematical tools they need and don't necessarily have good enough data sets -- a lot of the data they have has a lot of errors in it. A lot of the work involved in any ML activity is about cleaning up the data sets."

While robotic process automation and machine learning can eliminate the need for manual processes in the network, these tools are only as effective as the engineers developing and managing them. Herein lies the paradox of automation -- the more advanced the control system is, the more crucial the contribution of the human operator, explains Crawshaw. When an automated system fails, it still falls back on human oversight to intervene.

*(Editor's note: An earlier version of this article incorrectly stated the number of employees writing bots for AT&T. The number is 2,000. In addition, the company has not officially stated the savings that it collects from its use of bots.)

— Kelsey Kusterer Ziser, Senior Editor, Light Reading

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