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Duration 14 hours
Course Outline
The AI Observability Ecosystem
- Transitioning from dashboards to dialogue: the move toward AI-augmented observability
- Key LLM capabilities in observability: summarization, reasoning, and pattern recognition
- Architectural strategies: embedding AI into current observability stacks
Natural Language Telemetry Querying
- Text-to-PromQL: converting natural language into monitoring queries
- NL querying capabilities for Elasticsearch, OpenSearch, and Loki log stores
- Generating SQL from natural language for structured telemetry data
- Developing a query assistant agent equipped with tool usage and contextual awareness
LLM-Enhanced Log Analysis
- Automated log parsing and structuring leveraging LLMs
- Detecting anomalies in log streams via embedding similarity
- Clustering logs and discovering patterns at scale
- Creating human-readable explanations from raw log sequences
Intelligent Alerting and Incident Enrichment
- Correlating and deduplicating alerts using semantic understanding
- Gathering automated incident context from runbooks, historical incidents, and documentation
- Routing alerts intelligently based on content comprehension and team expertise
- Mitigating alert fatigue through AI-driven noise reduction
AI-Supported Root Cause Analysis
- Generating hypotheses by correlating multi-source telemetry
- Evidence chaining: linking symptoms across metrics, logs, and traces
- Guided troubleshooting via interactive AI diagnostic sessions
- Constructing a root cause analysis agent with progressive investigation capabilities
Automated Incident Response and Communication
- Producing incident summaries and status updates derived from telemetry
- Automating postmortem drafting with timeline reconstruction
- Tailoring stakeholder communication for both technical and executive audiences
- Suggesting runbooks and automated remediation actions
Machine Learning for 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 via embeddings
- Training and deploying lightweight ML models alongside observability pipelines
Production Deployment and Ethics
- Addressing latency and cost implications for real-time AI observability
- Data privacy: preventing LLMs from leaking sensitive telemetry
- Human oversight: identifying when AI diagnoses require operator validation
- Evaluating impact through MTTD, MTTR, and on-call experience metrics
Requirements
- Practical experience with observability platforms such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Understanding of log management and metrics fundamentals.
- Foundational Python scripting skills for data processing.
Target Audience
- SRE and observability engineers integrating AI-enhanced tools.
- Platform engineers developing advanced monitoring pipelines.
- DevOps leads assessing LLM integration within incident workflows.
