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Duration 14 hours
Course Outline
The AI Observability Landscape
- Moving from dashboards to conversations: the transition towards AI-augmented observability.
- LLM capabilities pertinent to observability, including summarization, reasoning, and pattern matching.
- Architecture patterns for embedding AI into existing observability stacks.
Natural Language Telemetry Querying
- Text-to-PromQL: converting natural language into monitoring queries.
- NL querying for Elasticsearch, OpenSearch, and Loki log stores.
- SQL generation from natural language for structured telemetry data.
- Constructing 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 leveraging embedding similarity.
- Log clustering and pattern discovery at scale.
- Generating human-readable explanations derived from raw log sequences.
Intelligent Alerting and Incident Enrichment
- Alert correlation and deduplication using semantic understanding.
- Automated gathering of incident context from runbooks, past incidents, and documentation.
- Smart alert routing based on content analysis and team expertise.
- Mitigating alert fatigue through AI-driven noise reduction.
AI-Assisted Root Cause Analysis
- Hypothesis generation via correlation of multi-source telemetry data.
- Evidence chaining: linking symptoms across metrics, logs, and traces.
- Guided troubleshooting supported by interactive AI diagnosis sessions.
- Developing a root cause analysis agent featuring progressive investigation capabilities.
Automated Incident Response and Communication
- Generating incident summaries and status updates from telemetry data.
- Automated drafting of postmortems with timeline reconstruction.
- Tailored stakeholder communication for both technical and executive audiences.
- Suggestions for runbooks and automated remediation recommendations.
ML for Observability
- Time-series forecasting for capacity planning and anomaly prediction.
- Utilizing 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
- Addressing latency and cost considerations for real-time AI observability.
- Data privacy: ensuring LLMs do not expose sensitive telemetry data.
- Human oversight: identifying when AI diagnoses require operator validation.
- Measuring impact through metrics such as MTTD, MTTR, and on-call experience indicators.
Requirements
- Hands-on experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Familiarity with the core concepts of log management and metrics.
- Basic proficiency in Python scripting for data processing tasks.
Target Audience
- SRE and observability engineers looking to adopt AI-enhanced tools.
- Platform engineers developing next-generation monitoring pipelines.
- DevOps leaders evaluating the integration of LLMs into incident management workflows.
