What are some good AIOps solutions

AIOps

AIOps leverages big data, machine learning, and analytics to help ITOps predict, find, and fix problems faster.

Manage Complexity with AIOps Software

AIOps tools apply machine learning and advanced analytics to discover patterns in monitoring, capacity, service desk and automation data in hybrid on-premise and multi-cloud environments. The introduction of AIOps enables the teams responsible for IT operations and observability to:

  • Using AIOps, machine learning, and anomaly detection to improve performance and availability on-premise and in the cloud
  • Reduce event noise and business-critical problems
  • Publish applications faster and accelerate DevOps processes
  • Proactive detection of problems and quick identification of the causes to shorten the MTTR
  • Model and predict workload capacity requirements to optimize resource utilization and costs

Key requirements for AIOps software

Implementing an AIOps strategy is much more than just a better analysis of existing data. Creating the foundation for a machine learning system that delivers continuous insights requires:

BMC is a proven leader in AIOps

BMC solutions offer machine learning and advanced analytics as part of a holistic monitoring, event management, capacity and automation solution. They provide use cases for AIOps to bring IT operations up to the speed the digital enterprise needs.

  • Reduction of event noise by 90%
  • 40% reduction in incidents through preventive alerts
  • Find the cause 60% faster
  • Automated error correction to reduce the MTTR by 75%

Open data access

Observability teams need to be able to leverage massive amounts of data and events across multiple technologies and recording systems as the foundation for a successful AIOps strategy. The key requirements include:

  • Monitoring of distributed applications in on-premise, cloud and container environments
  • Consistent view of data across different levels of the application stack
  • Data-independent monitoring, including the transfer of data from other monitoring tools

Machine learning

Ultimately, IT analysis is about pattern matching. Machine learning applies the computing power and speed of machines to the detection and correlation of patterns in IT data. It does this faster than human carers and dynamically changes the algorithms used by analytics based on data changes.

  • Learning behavior under normal conditions
  • Dynamic baselines go beyond static thresholds
  • Detection of anomalies based on learned patterns

    AIOps and ITSM automation

    The tangible value of AIOps arises through the use of the extensive knowledge from machine learning and analyzes. This enables maximum business value through increased automation and breaking the silos between ITOM and ITSM. Some useful use cases for automation through AIOps are: