Your network is the heartbeat of the user experience. When it goes down, employees and clients get disappointed, which can cause reduced productivity, deserted sales, and other unwanted business outcomes. And there’s additional frustration if users should call or send an assistance ticket to IT. This additional action slows resolution. How can an organization prevent this scenario?The answer is experience-first networking, that includes a strong network facilities with visibility into how it’s carrying out. This method supplies the best possible experience for network operators who must keep the network heartbeat thrumming, along with end users who keep business moving.There are two crucial components of experience-first networking. First is having a cloud-native, microservices-based architecture in which functions like authentication, area, and the visitor network are disaggregated. This enables personnel to rapidly identify and patch the function causing the concern so the whole network service is not affected.The second is a well-stocked information science tool kit that forms the structure of an expert system(AI)driven network. It needs to consist of, for instance, conversational AI, deep learning capabilities such as a neural network and transformer-based language designs, and machine learning (ML) functionalities. Bringing it all together The Juniper Mist AI ™ option integrates a microservices cloud architecture with AI functionality to make networking predicable, trustworthy, and quantifiable with thorough
visibility into the user experience.Underpinning Mist AI is its cloud platform, which offers network teams a single point of control from which to handle routers, access points, switches, and so on. Its microservices architecture makes it easy to quickly repair bugs, scale services, and add or remove
features.The Mist AI platform utilizes open APIs, making it completely programmable for automated, smooth combination across LANs, WANs, security solutions, and more.In addition, the Mist AI cloud utilizes AI and data science to evaluate network information and provide actionable insights,
such as: Quick root-cause identification utilizing ML to correlate events The ability to identify the possible effect of negative trends using time-series anomaly detection
Fast results from complex queries utilizing natural language processing At the heart of Juniper’s AI-driven network experience is Marvis, a digital network assistant that has been threaded through the service provider’s changing, routing
