What is AIOps?
What Is AIOps?
AIOps, or artificial intelligence for IT operations, is an industry term coined by Gartner. It describes technology platforms and processes that enable IT teams to make faster, more accurate decisions and respond to network and systems incidents more quickly.
AIOps contextualizes large volumes of telemetry and log data across an organization’s IT infrastructure in real-time or near-real-time. It then combines it with relevant historical data to generate actionable insights. AIOps is the embodiment of an assistant that has deep knowledge of the IT and network environment along with the ability to use that knowledge to provide real-time analysis and execute or recommend next steps.
Why Is AIOps Important?
AIOps increases the efficiency and performance of individual applications and services. Organizations using AIOps as part of their automated infrastructure and operations workflows are improving everything from security and outage incident response times to infrastructure purchases. Those just starting with AIOps see it as an investment in performance analysis, anomaly detection, and event correlation that gives them the ability to predict future network-impacting events.
Use Cases, Benefits, and Outcomes
Ideally, AIOps is invisible to the end-user and integrated into the administrator’s everyday management tools. AIOps is a component of numerous products and services used both by Juniper and its customers on a regular basis today.
Putting AIOps to Work
Getting started with AIOps is not much different from deploying any other infrastructure analytics package. The AIOps platform must be connected to the infrastructure that it will monitor, after which discovery and learning begin. Preliminary insights become available once enough data has been ingested during the AI learning process.
The AIOps platform analyzes the IT environment, then offers root cause analyses of problems as they occur. The final phase of AIOps incorporation into an organization’s workflow is automation. Once the AIOps platform has learned enough, it can begin automatically remediating simple problems.
AIOps Solution Deployment Stages
Integrating AIOps into IT workflows is a relatively simple matter that quickly results in basic automation and remediation. These functions grow increasingly sophisticated as the platform ingests more data and continues to learn.
Real-World Networking Value
Industry-leading AIOps platforms provide capabilities across wired, wireless, WAN, and security domains, offering service assurance end to end. They increase efficacy and drive customer success by tuning algorithms and leveraging clean, contextualized data sources.
AIOps is not merely about being able to do what you do today better. The true value is about managing the complexity of IT infrastructure as it surpasses what humans can manage alone, even with the best non-AI tools at their disposal.
AIOps offers a number of benefits to operators of enterprise and commercial networks.
- Accelerates time to incident resolution
- Consolidates and analyzes data from multiple sources
- Observes and learns the details of each unique operational environment
- Provides assessments based on a calculated quality of experience (QoE)
- Provides a conversational interface using natural language processing (NLP)
Internal Juniper Implementation
At Juniper, we leverage a microservices cloud architecture and Marvis, our AI engine and virtual network assistant, to handle support tickets received by the Juniper customer support team. This inverted customer support model provides the ability to inform customers about issues, such as the necessity of a hardware return, before a failure occurs.
Marvis uses closed-loop feedback and is retrained with the right data to continue increasing efficacy. In a well-thought-out AIOps solution, a vendor should have the same information as the customer so that it knows when the customer is having a problem.
Juniper’s AI-Driven Customer Support Model
The Marvis virtual network assistant increases efficacy by leveraging AI-driven support.
Simply put, AI and cloud computing are changing the support model between customer and vendor. In Figure 3, you see the total aggregated tickets coming in, illustrated by the dotted line. Customer growth is shown in terms of devices, sites, and organizations added while trouble tickets remain relatively unchanged. The figure illustrates how AIOps impacts customer help desk tickets, resulting in fewer escalations, fewer incoming tickets, and faster troubleshooting and resolution.
Stemming the Tide of Trouble Tickets
Juniper AIOps impact: As the number of customers and network complexity grow, trouble tickets fall or remain at the same level.
AIOps FAQs
What problems does AIOps solve?
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. Above all, AIOps democratizes the ability to troubleshoot among IT personnel with different levels of expertise, increasing overall operations efficiency across the team.
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.
What AIOps solutions does Juniper offer?
Juniper is a recognized leader in AIOps. Our wired access, wireless access, and SD-WAN solutions, for example, are all unified by Mist AI. These AIOps solutions simplify end-to-end troubleshooting, Self-Driving Network™ operations, and client-to-cloud insight into customer experiences. In addition, Marvis, the industry’s first AI-driven virtual network assistant, has an interactive conversational interface that provides simple recommendations to complex problems. All of these tools, driven by Mist AI, can save you time and money while maximizing the value of your network infrastructure.
