Operations teams are undergoing a paradigm shift and embracing big data, modern machine learning, and other advanced analytics technologies to boost operations efficiency with proactive, personal, and dynamic insight.
Recently, Gartner coined the term AIOps (artificial intelligence for IT operations) to capture the spirit of these changes which is disrupting the trends of large-scale device monitoring.
According to Gartner, AIOps is going to drive a major change in operations over the next few years. Both Communications Service Providers (CSPs) and enterprises will substantially benefit from this shift as they undergo digital transformation.
Our client has large-scale enterprise networks to support their business customers and millions of consumers. With the benefits, there were challenges to monitor and successfully administrator the networks, traffic problems and application malfunctions. They needed
Trying to monitor network segments while looking at traffic or application performance is difficult without the use of emerging technologies. They needed a solution that could capture all the network operations data across different application layer and show insights on top of it.
As the network and its applications became more complex, so did its monitoring needs. This is where AIOps enters the picture. AIOps enable us to leverage artificial intelligence (AI) and machine learning to derive real-time insights and automate tasks to augment IT operations teams. This requires a mindset shift for teams to move away from siloed monitoring tools and start building a proactive approach to performance monitoring. When teams embrace an AIOps mindset, endless debugging tasks will be a thing of the past. AI-based systems is accelerating root cause analysis, predict performance issues, recommend optimizations, and automate fixes in real-time.
Our algorithm-based approach reduces large volumes of data into actionable bits of information and assist in the learning and codification of the knowledge. For example, it would take numerous hours for a human to manually analyze and correlate every event in your production environment; however, the algorithms can accomplish the task in a matter of seconds.
Combining machine learning with human skills and knowledge offers an optimal approach to delivering the target SLA and boost operational efficiency in a large-scale organization.
Our solution constantly learns, captures knowledge gained from an operator’s experience and then automatically store and share t for future reuse through features such as probable root cause analysis and algorithmic knowledge. We delivered many business benefits:
- Enrich AIOps data with service and device attributes such as service name, service components, and topology to help correlate them across the service and infrastructure layers
- Prioritize based on business and service impact which helps to characterize the impact of the issues that require attention from the operator
- Automate service assurance through a model-driven approach.
- Automatic noise reduction
- Algorithmic correlation: A variety of patented algorithmic and machine learning techniques build clusters of related alerts automatically, identifying unique situations without the need for laborious development and time-intensive maintenance of rules, filters, or inventory-based service maps
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