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Duration 14 hours
Course Outline
The AI Observability Landscape
- From dashboards to dialogues: the evolution toward AI-augmented observability
- Relevant LLM capabilities for observability: summarization, reasoning, and pattern matching
- Architectural patterns: integrating AI into existing observability stacks
Natural Language Telemetry Querying
- Text-to-PromQL: converting natural language into monitoring queries
- Natural language querying for Elasticsearch, OpenSearch, and Loki log stores
- Generating SQL from natural language for structured telemetry
- Creating a query assistant agent with tool usage and contextual awareness
LLM-Driven Log Analysis
- Automated log parsing and structuring using LLMs
- Detecting anomalies in log streams via embedding similarity
- Large-scale log clustering and pattern identification
- Producing human-readable explanations from raw log sequences
Intelligent Alerting and Incident Enrichment
- Alert correlation and deduplication through semantic understanding
- Automated incident context collection from runbooks, historical incidents, and documentation
- Intelligent alert routing based on content comprehension and team expertise
- Mitigating alert fatigue via AI-driven noise reduction
AI-Assisted Root Cause Analysis
- Generating hypotheses through multi-source telemetry correlation
- Evidence chaining: linking symptoms across metrics, logs, and traces
- Guided troubleshooting via interactive AI diagnosis sessions
- Developing a root cause analysis agent with progressive investigation capabilities
Automated Incident Response and Communication
- Creating incident summaries and status updates from telemetry data
- Automated postmortem drafting with timeline reconstruction
- Tailoring stakeholder communication for technical and executive audiences
- Providing runbook suggestions and automated remediation recommendations
Machine Learning for Observability
- Time-series forecasting for capacity planning and anomaly prediction
- Using foundation models for zero-shot anomaly detection on metrics
- Embedding-based service dependency mapping and topology discovery
- Training and deploying lightweight ML models alongside observability pipelines
Production Deployment and Ethics
- Considerations for latency and cost in real-time AI observability
- Data privacy: preventing LLMs from leaking sensitive telemetry
- Human oversight: scenarios requiring operator validation of AI diagnoses
- Measuring impact: MTTD, MTTR, and on-call experience metrics
Requirements
- Proficiency with observability platforms such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Understanding of log management and metrics principles.
- Foundational skills in Python scripting for data processing.
Target Audience
- SRE and observability engineers implementing AI-enhanced tooling.
- Platform engineers constructing next-generation monitoring pipelines.
- DevOps leads assessing LLM integration within incident workflows.
