AIOps Foundation study plan

Define AIOps in your own words and distinguish it from monitoring, observability, and automation

Trace the evolution from siloed domain monitoring (network, application, infrastructure) through event correlation and ITOA to modern AIOps platforms

Identify the operational pain points (alert storms, tool sprawl, mean-time-to-resolution pressure) that drove AIOps adoption

Map at least five historical milestones or technology shifts to the capability each one enabled

Compare AIOps platform architectures: data ingestion, normalization, correlation, and action layers

Explain how telemetry sources (metrics, logs, traces, events, tickets, CMDB) feed an AIOps pipeline

Describe the role of topology and dependency mapping in reducing noise

Contrast rule-based correlation with statistical and ML-driven correlation

Explain why volume, variety, velocity, and veracity matter for operational data and how each affects model choice

Describe storage and streaming patterns (time-series stores, log indexing, message queues) used in AIOps backends

Distinguish structured, semi-structured, and unstructured telemetry and the preprocessing each requires

Summarize data quality risks such as missing data, clock skew, and cardinality explosion

Distinguish supervised, unsupervised, and reinforcement learning use cases in operations

Explain anomaly detection approaches: statistical baselines, clustering, isolation forests, and time-series forecasting

Describe event correlation and clustering techniques used to group related alerts

Identify where natural language processing applies to log parsing and ticket classification

Explain model lifecycle concerns in operations: training data drift, retraining cadence, and evaluation

Compare MLOps practices with AIOps practices and identify where they overlap and diverge

Describe how AIOps consumes and supports DevOps feedback loops such as CI/CD and incident response

Outline how SLOs, error budgets, and toil reduction connect Site Reliability Engineering to AIOps

Map AIOps capabilities onto the SRE workflow from detection through diagnosis, remediation, and postmortem

Explain how AIOps supports incident management, change correlation, and capacity planning

Describe the cultural prerequisites for AIOps adoption, including trust in automation and human-in-the-loop design

Identify where AIOps can reinforce or undermine existing DevOps and SRE practices

Enumerate concrete benefits of AIOps with the metric each one should move

Identify implementation challenges: data readiness, tool integration, skills gaps, false positives, and change management

Describe a phased adoption approach from assisted detection to automated remediation

Explain how to measure AIOps success against baseline MTTR, alert volume, and toil hours

Apply ethical frameworks to AIOps scenarios involving automated remediation, employee monitoring, and data retention

Evaluate bias, transparency, accountability, and blast-radius risks in automated operations

Synthesize all four domains into a coherent position on responsible AIOps adoption

Practice timed recall across the full syllabus and close gaps identified in earlier weeks