DevOps, DevSecOps, DataOps, MLOps all have gained traction and stabilized in the past few years to the point where they are at least known to everyone. The question is tough, what are AIOps and Platform Ops?
AIOps is more meaningful now than before because we now have data that is consistent and valid and larger in volume, allowing more useful data to be available to machine learning pipelines, for generating advanced analytics, enhancing predictions, etc. finding patterns in the data sets.
Enterprises have started adopting AIOps platforms to compete with and replace some traditional monitoring tool categories. For example, monitoring IaaS and observability is being done entirely within AIOps platforms, especially if the enterprise has its entire IT footprint in the cloud.
AIOps platforms enhance a broad range of IT practices, including I&O, DevOps, SRE, and Service Management. However, the more focused outcomes are within the I&O domain and include anomaly detection, diagnostic information, event correlation, and root cause analysis (RCA) to improve monitoring, service management, and automation tasks.
One of the biggest takeaways from a recent Gartner report is the division of the AIOps platform offerings into two categories,
- Domain-centric solutions
Gartner says that “requirements for increased flexibility for processing highly diverse datasets are having a significant impact on the market and shifting AIOps platforms toward domain-agnostic functionality.” This is also being driven by the flexibility that domain-agnostic platforms offer when it comes to consuming increasingly diverse datasets across a progressive roadmap stretching from three to five years.
What is AIOps?
- Collect and aggregate the huge and ever-increasing volume of operations data generated by multiple IT infrastructure components, applications, and performance monitoring tools
- Intelligently sift ‘signals’ out of the ‘noise’ to identify significant events and patterns related to system performance and availability issues
- Diagnose root causes and report them to IT for rapid response and remediation or in some cases, automatically resolve these issues without human intervention
Why we need AIOps?
Traditional domain-based IT management solutions can’t keep up with the volume, can’t intelligently sort the significant events out of the crush of surrounding data, can’t correlate data across different but interdependent environments, and can’t provide real-time insights and predictive analysis that IT operations teams need to respond to issues fast enough to meet user and customer service level expectations.
Enter AIOps, which provides visibility into performance data and dependencies across all environments, analyzes the data to extract significant events related to slow-downs or outages, and automatically alerts IT staff to problems, their root causes, and recommended solutions.
- Historical performance and event data
- Streaming real-time operations events
- System logs and metrics
- Network data, including packet data
- Incident-related data and ticketing
- Related document-based data
Benefits of AIOps
- Achieve faster mean time to resolution (MTTR) : By cutting through IT operations noise and correlating operations data from multiple IT environments, AIOps can identify root causes and propose solutions faster and more accurately than humanly possible. This enables organizations to set and achieve previously unthinkable MTTR goals. For example, telecommunications provider Nextel Brazil was able to use AIOps to reduce incident response times from 30 minutes to less than 5 minutes
- Go from reactive to proactive to predictive management : Because it never stops learning, AIOps keeps getting better at identifying less-urgent alerts or signals that correlate with more-urgent situations. This means it can provide predictive alerts that let IT teams address potential problems before they lead to slow-downs or outages
- Modernize your IT operations and your IT operations team : Instead of being bombarded with every alert from every environment, AIOps operations teams only receive alerts that meet specific service level thresholds or parameters – compete with all the context required to make the best possible diagnosis and take the fastest corrective action. The more AIOps learns and automates, the more it helps ‘keep the lights on’ with less human effort, and the more your IT operations team can focus on tasks with greater strategic value to the business
- Digital Transformation : Digital transformation is what creates the IT complexity (e.g., multiple environments, virtualized resources, dynamic infrastructure) that AIOps is designed to tackle. The right AIOps solution gives an organization more freedom and flexibility to transform based on strategic business goals, without worrying about the IT operations burden
- Cloud Adoption/Migration : For most organizations, cloud adoption is gradual, not wholesale, resulting in a hybrid multi-cloud environment (private cloud, public cloud, multiple vendors), with multiple interdependencies that can change too quickly and frequently to document. By providing clear visibility into these interdependencies, AIOps can dramatically reduce the operational risks of cloud migration and a hybrid cloud approach
- DevOps Adoption : DevOps speeds development by giving development teams more power to provision and reconfigure infrastructure, but IT still has to manage that infrastructure. AIOps provides the visibility and automation IT needs to support DevOps without much additional management efforts
Major Players in AIOps
- IBM/Data Dog/Prometheus
- Miracle Expertise
According to Gartner (based on the IBM website) – The average cost of critical IT incidents per month, per organization, totals around $1.2M. Starting this year, 50% of enterprises will be actively adopting AI to augment their Application Performance Monitoring (APM) tools to catch incidents before they become critical.