Did you know how engineers at Hughes are using ML and AI across their networks to improve the customer experience? 

Today we will take a look at both terms, their utility, and some of their history, and we will see how they can help optimize network management in order to improve your experience as a customer. 

WHAT DOES ML AND AI MEAN AND HOW THEY WORK?

Machine Learning (ML) is a technique to forecast and predict events based on historical data that has been collected over time, this data is then used to train a mathematical model that can predict future events with a reasonable degree of accuracy. While Artificial Intelligence (AI) determines the automatic action that can be taken when a machine learning (ML) agent detects an anomaly, in other words, artificial intelligence (AI) takes machine learning one step further. 

The term ML was first coined in 1959 by Arthur Samuel, an AI pioneer, and developer of one of the first gaming programs. However, it was only in recent years that it became widely used, as ML shaped the technology-based capabilities that are so familiar to us now, such as self-correction and search page rankings. 

This is how the growing amount of data available from the cloud, the billions of connected devices and the Internet of Things, can now be digested by ML models and applied to network management tasks, designating ML agents to use artificial intelligence as a means of network monitoring and for the customer’s sake. 

HOW CAN ML AND AI OPTIMIZE NETWORK MANAGEMENT?

An ML agent can prompt network engineers and customer service agents to resolve an issue before a problem occurs, or in some cases, artificial intelligence can determine an automatic action to pre-empt the event. With both the ML and the AI, network monitoring is no longer an «on-demand» activity, where the customer calls about a problem and someone troubleshoots the issue. Instead, ML in the cloud monitors customer networks in real-time, all the time. In that way, ML can predict network congestion and assist with traffic classification. This is critical for tasks such as capacity planning, security and intrusion detection, quality of service, performance monitoring, application prioritization, and efficient resource management, all of which are used by Hughes to help deliver a better customer experience. 

HOW DOES HUGHES USES ML AND AI TO ENHANCE THE CUSTOMER EXPERIENCE?

Hughes also uses artificial intelligence and machine learning to see what transports might be available that are most aligned to the business needs at a retail location, whether cable, fiber, or DSL, the ML is also used to predict whether a satellite terminal will enter a degraded state, even before the terminal can pick up and report it. The algorithms send an alert to a network engineer to solve the problem. When the ML agent detects an anomaly, such as a problem site, it sends an alert to a Hughes team member using a variety of formats. Alerts can be placed on the dashboard, or via email, chatbot, and even a voice personal assistant (like Alexa and Siri). The Hughes team member can then interact with the ML agent to learn more details, with the ML agent pulling additional data from the cloud to provide more information and answer questions. The ML can also be applied outside of day-to-day network operations and free up the network engineer to do higher-value tasks.

Additionally, Hughes is making a new AI for IT operations (AIOps) feature commercially available for enterprise wide-area networks. This AIOps feature has already been in use across 32,000 managed sites, as part of the HughesON™ Managed Network Services. AIOps is used to predict and “self-heal” network anomalies before they can cause service disruptions, approximately 70% of the cases are automatically corrected.

For consumer networks, like HughesNet®, ML/AI network monitoring identifies certain types of traffic – such as video – and applies a data-saving feature to maximize the customer’s data plan each month. AI is also used to ensure that HughesNet installations are completed correctly. Our installers send photos of completed installations, and the system automatically examines them for any issues that need to be addressed.

At Hughes, ML/AI innovations are in use across our increasingly complex customer networks, helping to optimize the explosion of cloud-based services, diverse hybrid environments, mobility services, and the rise in Software-Defined Wide Area Network (SD-WAN) technologies. All this leads to higher customer satisfaction as they won’t experience an incident or outage. 

Actively incorporating the latest innovations in ML/AI, Hughes streamlines network management, improves efficiencies, and enhances the customer experience.

Did you know how engineers at Hughes are using ML and AI across their networks to improve the customer experience? 

Today we will take a look at both terms, their utility, and some of their history, and we will see how they can help optimize network management in order to improve your experience as a customer.