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Duration 14 hours
Course Outline
The Landscape of AI Observability
- Shifting from dashboards to conversations: the evolution toward AI-augmented observability
- LLM capabilities relevant to observability: summarization, reasoning, and pattern matching
- Architecture patterns: embedding AI into existing observability stacks
Natural Language Telemetry Querying
- Text-to-PromQL: translating natural language into monitoring queries
- Natural language querying for Elasticsearch, OpenSearch, and Loki log stores
- Generating SQL from natural language for structured telemetry
- Building a query assistant agent with tool use and context awareness
LLM-Powered Log Analysis
- Automated log parsing and structuring using LLMs
- Anomaly detection in log streams via embedding similarity
- Log clustering and pattern discovery at scale
- Generating human-readable explanations from raw log sequences
Intelligent Alerting and Incident Enrichment
- Alert correlation and deduplication with semantic understanding
- Automated gathering of incident context from runbooks, past incidents, and documentation
- Smart alert routing based on content understanding and team expertise
- Reducing alert fatigue through AI-driven noise reduction
AI-Assisted Root Cause Analysis
- Hypothesis generation from multi-source telemetry correlation
- Evidence chaining: connecting symptoms across metrics, logs, and traces
- Guided troubleshooting with interactive AI diagnosis sessions
- Developing a root cause analysis agent with progressive investigation capabilities
Automated Incident Response and Communication
- Generating incident summaries and status updates from telemetry data
- Automated postmortem drafting with timeline reconstruction
- Tailoring stakeholder communication for both technical and executive audiences
- Runbook suggestions and automated remediation recommendations
Machine Learning for Observability
- Time-series forecasting for capacity planning and anomaly prediction
- 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
- Latency and cost considerations for real-time AI observability
- Data privacy: ensuring LLMs do not leak sensitive telemetry
- Human oversight: recognizing when AI diagnosis requires operator validation
- Measuring impact: MTTD, MTTR, and on-call experience metrics
Requirements
- Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Familiarity with log management and metrics concepts.
- Basic Python scripting skills for data processing.
Audience
- SRE and observability engineers adopting AI-enhanced tooling.
- Platform engineers building next-generation monitoring pipelines.
- DevOps leads evaluating LLM integration into incident workflows.
