OpsRamp announced OpsQ Recommend Mode, a capability for first-response and incident remediation, as part of the OpsRamp Winter 2020 Release.
OpsQ Recommend Mode lets digital operations teams use predictive analytics to reduce mean-time-to-resolution (MTTR).
Other artificial intelligence for IT operations (AIOps) innovations in the release include visualization of alert similarity patterns and new alert stats widgets to provide transparency into machine learning-driven decisions.
The OpsRamp Winter 2020 Release also introduces 19 new cloud monitoring integrations for Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), along with dynamic topology maps for Azure and GCP. Highlights of the Winter 2020 Release include:
AIOps: OpsQ is OpsRamp’s intelligent event management, alert correlation, and remediation engine. New AIOps capabilities help IT operations teams ingest, analyze, and extract comprehensive insights for real-time event and incident management:
- OpsQ Recommend Mode. The Winter 2020 Release introduces the OpQ Bot and a new Recommend Mode for alert escalation policies so that IT teams can drive faster incident response with auto-suggested actions. OpsQ Recommend Mode lets IT teams stay in control by using explainable and transparent analytical recommendations for first-response and incident creation.
- Visualization of Alert Seasonality Patterns. Many alerts in IT environments recur at a predictable frequency. OpsRamp OpsQ can learn such seasonality patterns and automatically suppress these recurring alerts. With this release, IT teams can visualize seasonality patterns that OpsQ has learned. This visibility helps IT teams understand the auto-suppress decisions that OpsQ makes and trace recurring alert patterns to underlying IT activity.
- Alert Stats Widget. The Alert Stats widget shows the total number of raw events, correlated alerts, inference alerts, auto-ticketed alerts, and auto-suppressed alerts handled by the OpsQ event management engine. This widget shows how OpsRamp OpsQ reduces event volume at each stage so that IT teams can build more confidence in machine learning-based techniques for alert optimization.
Multi-Cloud Monitoring and Management: OpsRamp currently offers 120+ integrations across leading cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The OpsRamp Winter 2020 Release drives full-stack visibility for multi-cloud workloads with 19 new cloud monitoring integrations as well as dynamic topology maps for Azure and GCP:
- Deeper Cloud Monitoring. OpsRamp adds monitoring support for 4 AWS, 7 Azure, and 8 GCP cloud services:
AWS – Transit Gateway, AppSync, CloudSearch, and DocumentDB
Azure – Application Insights, Traffic Manager, Virtual Network, Route Table, Virtual Machine Scale Sets, SQL Elastic Pool, and Service Bus
GCP – Cloud BigTable, Cloud Composer, Cloud Filestore, Firebase, Cloud Memorystore for Redis, Cloud Run, Cloud TPU, and Cloud Tasks
- Cloud Topology Maps. In addition to AWS cloud topology maps, OpsRamp now offers automated topology discovery and mapping for Azure and GCP. IT teams can apply cloud topology maps to analyze the impact of changes in their multi-cloud environments. Cloud topology is also applied in OpsQ’s event correlation engine to increase the accuracy of machine learning models.
Hybrid Discovery and Synthetic Monitoring: The OpsRamp Winter 2020 Release introduces new platform capabilities for agentless discovery and synthetic monitoring:
- Agentless Discovery and Monitoring for Windows Servers. While OpsRamp offers agentless discovery for Linux and VMware compute, network, and storage resources, the Winter 2020 Release introduces agentless discovery and monitoring for Windows compute resources. Enterprises with remote offices can manage their distributed Windows infrastructure in a secure and frictionless way with agentless monitoring.
- Synthetic Monitoring. OpsRamp’s enhanced synthetic monitoring provides deeper insights and analysis for troubleshooting multi-step transactions. Application owners can break down each synthetic transaction and gain visibility into the performance of each step in a web transaction.
“While machine learning models for IT operations have generated considerable excitement among technology decision-makers, customers would like to understand how a black box model works before ceding control, ” said Bhanu Singh, SVP of Engineering and DevOps at OpsRamp. “The Winter 2020 Release ensures greater transparency of machine learning models for intelligent event and incident management, along with enhanced monitoring capabilities for leading public cloud platforms.”
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