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Duration 14 hours
Course Outline
The AI Observability Ecosystem
- Evolution from dashboards to dialogue: embracing AI-augmented observability
- LLM capabilities applicable to observability: summarization, logical reasoning, and pattern recognition
- Architectural strategies: embedding AI into established observability stacks
Querying Telemetry via Natural Language
- Text-to-PromQL: converting natural language into monitoring queries
- Natural language querying for Elasticsearch, OpenSearch, and Loki log repositories
- Generating SQL from natural language for structured telemetry data
- Developing query assistant agents with tool utilization and context awareness
LLM-Enhanced Log Analysis
- Automating log parsing and structuring using LLMs
- Detecting anomalies in log streams through embedding similarity
- Clustering logs and discovering patterns at scale
- Generating human-readable interpretations from raw log sequences
Intelligent Alerting and Incident Context
- Correlating and deduplicating alerts with semantic comprehension
- Automating the collection of incident context from runbooks, historical incidents, and documentation
- Intelligent alert routing based on content understanding and team specialization
- Mitigating alert fatigue through AI-driven noise reduction
AI-Facilitated Root Cause Analysis
- Generating hypotheses through the correlation of multi-source telemetry
- Evidence chaining: linking symptoms across metrics, logs, and traces
- Guided troubleshooting via interactive AI diagnosis sessions
- Constructing root cause analysis agents with progressive investigative capabilities
Automated Incident Response and Communication
- Crafting incident summaries and status updates from telemetry data
- Automating postmortem drafts with timeline reconstruction
- Tailoring stakeholder communication for both technical and executive audiences
- Suggesting runbooks and providing automated remediation recommendations
Machine Learning in Observability
- Time-series forecasting for capacity planning and anomaly prediction
- Utilizing foundation models for zero-shot anomaly detection on metrics
- Mapping service dependencies and discovering topology using embedding-based methods
- Training and deploying lightweight ML models alongside observability pipelines
Production Deployment and Ethical Considerations
- Considering latency and cost for real-time AI observability
- Data privacy: preventing LLMs from leaking sensitive telemetry
- Human oversight: identifying when AI diagnosis requires operator validation
- Measuring impact through MTTD, MTTR, and on-call experience metrics
Requirements
- Practical experience with observability platforms such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Working knowledge of log management and metrics concepts.
- Fundamental Python scripting skills for data processing.
Target Audience
- SRE and observability professionals integrating AI-enhanced tools.
- Platform engineers developing next-generation monitoring infrastructure.
- DevOps leaders assessing the integration of LLMs into incident management workflows.
