Understanding AIOps and what it means for you

Implementing AIOps for your Enterprise

AIOps merges machine learning algorithms and data science with IT operations to render established, proactive, and automated remediation capabilities. AIOps equips IT operations teams to deliver high-quality software services and superior customer experience.

By analyzing operational data spread across your enterprise that contains nuggets of useful information, AIOps can drive business outcomes with focussed decision-making, reduced time spent on complex analysis, and break down silos within the toolchain.

What is AIOps?

AIOps stands for Artificial Intelligence for IT Operations. It represents the multi-layered technology platform that automates and streamlines data collection and operates on a predictive model using machine learning to find trends in behaviors, patterns of data sets. It uses machine learning models to analyze data collected from IT assets; while providing actionable insights to operation teams. By applying AI techniques, advanced analytics, and machine learning to automate analysis and provide incident alerts or event predict outages based on the critical data scores/KPIs (logs, alerts, tickets) generated from various infrastructure and application components.

How AIOps can help

It leverages analytics and machine learning to remove extensive dependencies on human operators to collate logs, metrics, tickets, incidents, events, and API data to perform reliable and relevant actions in hyper-scaled IT environments providing a simplified view of operations.

The significant capabilities of AIOps

Automation & Alert Data Analysis

AIOps can automate routine practices like user requests and non-critical IT system alerts. AIOps platforms can also perform alert evaluation by handling data from multiple monitoring tools based on the models build on historical data. It can help the operations team in lowering down on the number of first-level responses they need to act.

Quick and Accurate Issue Identification

AIOps helps issue identification and handling. For example, NOC engineers can easily handle a known malware event on a non-critical system but might overlook an unusual download or process starting on a critical server since this is an unmonitored threat. AIOps places this scenario in the ‘out of norm’ category and addresses it differently while prioritizing the event on the critical system. It then allocates a lower priority to the known malware event by running an anti-malware function. Thereby both help prioritize tickets and streamline the resource utilization to ensure business operations.

Streamline Interactions using Metrics and Visualization

Each functional IT group receives relevant data and perspectives from AIOps. In the absence of AI-enabled operations, teams must share, parse, and process information by manually circulating data. AIOps can help build sophisticated metrics that take into account the dependencies between systems and services, and provide visualizations that help Operations teams to stay on top of things.

In addition to the above, the fourth crucial capability of AIOps that leverages enterprise performance is,

Anomaly Detection

Anomaly detection helps apply a set of robust techniques and predictive machine learning models to identify unusual behaviors and states in systems. AI-based anomaly frameworks reused today to detect unexpected spikes or aberrations in critical metrics such as response time, CPU usage, and memory usage.

DevOps technicians can use the anomaly data for better operational efficiency. Event logs are made of a lot of data which, when scrutinized, reveal patterns and unusual system events — these leverage forecasting and designing self-healing steps.

Using AIops, the operations team can leverage anomaly data for effective incident management and resolution in the following ways:

• Detect abnormal metric values to find issues that go undetected in your stack.
• Identify drastic changes in an important metric or process so you can examine the root cause.
• Reduce efforts to set or recalibrate thresholds across different monitors.
• Reduce the diagnosis and troubleshooting time required in your stack.

Moving towards Self-healing Applications with AIOps

With AIOps, systems could deliver automated insights that can be incorporated into the forthcoming software release. AIOps offers AI-driven analytics and machine learning capability to autonomously predict, recognize, and remediate incidents in complex hybrid IT environments by combing the elements of IaaS (Infrastructure as a Service), Dev Ops etc. With root cause identification capabilities, applications can automatically trigger a cascade of corresponding actions such as scaling up additional resources, initiating restarts, or failing over to an active resource.

The Elements of AIOps

Extensive and diverse IT data sources like events, metrics, logs, job data, tickets, monitoring, etc.

• A modern-day big data platform that supports real-time processing of streaming IT data (Performance Baselining).
• Rule application and pattern recognition that can be mapped to context while uncovering regularities and normalcies in the data (Anomaly detection).
• Domain algorithms that make it possible to achieve IT-specific goals like eliminating noise, correlating unstructured data, establishing baselines, alerting on abnormalities, and identifying the probable cause (automated root-cause analysis).
• Machine learning can automatically alter or create new algorithms on-the-fly based on the algorithmic analysis output and new data introductions.
• Artificial intelligence that can adapt to new and unknown elements in an environment and provide predictive insights.
• Automation that automatically creates and applies a response or improvement for identified issues and situations.

Implementing AIOps

While AIOps has recently caught the eye of technology enthusiasts in the IT radar, we at Cambridge have been leading with customer and self-implementation of such systems since last few years.

AIOps can demonstrate value and mitigate risks from deployment if introduced in small, carefully orchestrated steps. The development/operations team must decide upon an appropriate hosting model for the tool, such as on-site or as a service. IT staff must understand the need for AIOps and then be trained to deliver high-quality services.

Designing and implementing AIOps needs varied skillset of resources. We at Cambridge Technology have been helping our IT customers with these skillsets including Data Scientists, DevOps, Cloud Architects and Big Data. Before you decide on investing into AIOps platforms, it is essential to schedule a gap analysis to determine the current state of your IT systems. Reach out to us and we can help you define a roadmap. Learn More