AIOps Foundation flashcards

32 cards. Read the term and the definition here. The deck at the top of this page is optional.

AIOps definition

Gartner's term for applying big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and root-cause analysis across telemetry sources.

Gartner AIOps coinage

Gartner introduced the AIOps term in 2016, framing it as a platform category that aggregates big data from operations and applies analytics and ML to improve IT service management.

Pre-AIOps expert systems

1990s fault-management systems encoded human rules for event correlation. They failed to scale because rules required constant manual updates as infrastructure changed.

AIOps evolution stages

Progression from siloed monitoring to centralized aggregation, then ML-based correlation and anomaly detection, and finally closed-loop automated remediation with human oversight.

Cloud-scale telemetry driver

Microservices and containers multiplied metrics, logs, and traces beyond human triage capacity, making ML-assisted operations necessary rather than optional.

Rule-based versus learned correlation

Rule-based correlation uses static thresholds and hand-written logic; learned correlation infers baselines and relationships from historical data, adapting as systems change.

Three V's of big data

Volume, velocity, and variety describe data scale, arrival speed, and format diversity. AIOps telemetry exhibits all three, plus veracity challenges from noise.

Metrics, logs, traces, events

Four core telemetry types: numeric time series, unstructured text records, causal request graphs, and discrete state-change notifications. AIOps correlates across all four.

Stream versus batch processing

Stream processing handles telemetry in near-real-time for alerting; batch processing analyzes historical data for model training and long-term trend detection.

Autoencoder anomaly detection

A reconstruction-based method where a neural network learns normal patterns; high reconstruction error on new data signals an anomaly without requiring labeled incidents.

Unsupervised learning in AIOps

Clustering and density methods detect anomalies and group alerts without labels, which is essential because labeled incident data is scarce and expensive to produce.

Log clustering with NLP

Tokenization and embedding techniques group similar log messages into templates, reducing noise and enabling pattern-based anomaly detection across high-volume log streams.

Time-series forecasting

Models such as ARIMA or LSTM predict future metric values, supporting capacity planning and proactive alerts before thresholds are breached.

Precision and recall for alerting

Precision measures how many raised alerts are real incidents; recall measures how many real incidents were caught. Both matter for alert quality.

Feature engineering for AIOps

Deriving rolling statistics, seasonality indicators, topology relationships, and deployment markers from raw telemetry to improve model accuracy and interpretability.

DevOps and AIOps distinction

DevOps is a culture and practice for rapid software delivery; AIOps is an analytics capability that consumes the telemetry DevOps pipelines and platforms generate.

MLOps lifecycle governance

MLOps manages model training, versioning, deployment, and monitoring. AIOps models should follow MLOps practices to remain accurate and auditable over time.

SLO and error budget

A service-level objective sets a target reliability level; the error budget is the allowed shortfall. AIOps can compute SLIs and alert on burn rate.

AIOps and SRE toil

AIOps reduces repetitive operational toil through automation and correlation, but SRE teams retain ownership of SLOs, incident command, and reliability decisions.

CI/CD change correlation

Feeding deployment events from CI/CD pipelines into AIOps enables correlation between code changes and incident onset, accelerating root-cause identification.

Model drift versus alert drift

Model drift is degradation of ML prediction quality, an MLOps concern. Alert drift is degradation of operational signal quality, an AIOps concern. Both need feedback loops.

AIOps and ITSM integration

AIOps platforms enrich incident and change records in ITSM tools with correlated evidence, rather than replacing ticketing or change-management workflows.

Cross-team governance overlap

Model approval, audit trails, and rollback plans span MLOps, DevOps, and SRE teams, requiring shared policies for AIOps deployments.

Alert noise reduction

A primary AIOps benefit: deduplication and correlation collapse thousands of raw alerts into a small number of actionable incidents, reducing operator fatigue.

MTTR reduction

Faster mean time to resolution is a key AIOps outcome, achieved through automated correlation, root-cause suggestions, and faster incident triage.

Data quality challenge

Missing, inconsistent, or unlabeled telemetry degrades model accuracy. Normalization and data validation are prerequisites for reliable AIOps outcomes.

Tool sprawl challenge

Heterogeneous monitoring tools with different schemas require normalization and integration before cross-domain correlation and ML analysis are possible.

Skills gap challenge

Successful AIOps requires combined data science, operations, and domain expertise, a rare combination that slows adoption in many organizations.

Explainability and adoption

Operators resist opaque models. Explainable alerts and human-in-the-loop review increase trust and adoption of AIOps-driven recommendations.

Algorithmic bias in alerting

Models trained on biased data may systematically under-alert on certain services or customer segments. Regular outcome audits are an ethical requirement.

Privacy in log ingestion

Logs may contain personally identifiable information. Data minimization, masking, and access controls must be applied before ML ingestion to protect privacy.

Automated remediation guardrails

Closed-loop automation requires approval thresholds, rollback plans, and clear human ownership so that automated actions remain accountable and reversible.