Juniper AIOps-AI for IT Operations
Видавець: Marconi Wireless Inc
Transform IT for networking with artificial intelligence, machine learning, and data science.
AIOps analyzes and consolidates data from multiple sources. It observes and learns details from the environment and provides assessments based on overall quality of experience (QoE). In this way, AIOps is able to correlate network activities to determine and resolve problems before they’re noticed by end-users or IT operations staff.AIOps provides root cause analyses of problems as or before they occur based on machine learning (ML) algorithms and contextualized data.
What are the components of AIOps?
An AIOps platform uses ML algorithms and contextualized data to provide root cause analyses and automatically remediate simple problems in the network. AIOps requires an AI engine able to correlate events and ML algorithms that extract knowledge or patterns from a set of observations. A virtual network assistant using natural language processing (NLP) enhanced by natural language understanding (NLU) and language generation (LG) combine to offer a powerful conversational interface that can contextualize requests, accelerate troubleshooting, and make intelligent decisions or recommendations to streamline operations.
What are the key capabilities of AIOps?
- Problem isolation/root cause analysis – With the large volumes of data in today’s networks, it’s difficult to pinpoint problems raised in trouble tickets, much less those that haven’t been brought to the attention of IT. AIOps correlates events in real-time by processing contextualized data, allowing operations teams to identify and rectify issues in a timely manner.
- Data-driven decision-making – ML algorithms drive data-based analysis, which offers operational recommendations or remediations rather than predetermined responses to networking faults or anomalies. This data-centric approach improves operations staffs’ troubleshooting efficiency.
- Predictive reporting – AIOps predicts network behavior and offers recommendations or remediations for fixing degraded performance and other anomalies within the network. This fundamental shift benefits operations teams by allowing them to be proactive in managing network operations, rather than chasing down issues that have already had an impact on users and the business. As a result, IT frees up time once spent in fire-fighting mode to tackle future business objectives.