Get in Touch

Course Outline

The AI Observability Landscape

  • The evolution from static dashboards to AI-enhanced conversational interfaces for system monitoring
  • Application of large language model (LLM) capabilities, including summarization, logical reasoning, and pattern recognition, to observability tasks
  • Architectural frameworks for integrating artificial intelligence into established observability infrastructure

Natural Language Telemetry Querying

  • Converting natural language inputs into PromQL commands for metrics monitoring
  • Implementing natural language querying capabilities for Elasticsearch, OpenSearch, and Loki log management systems
  • Generating SQL queries from natural language to facilitate structured telemetry retrieval
  • Developing query assistance agents equipped with tool utilization and contextual awareness for government workflows

LLM-Powered Log Analysis

  • Automating log parsing and data structuring through LLM integration
  • Identifying anomalies within log streams using embedding similarity techniques
  • Scaling log clustering and pattern discovery across large datasets
  • Producing clear, human-readable explanations derived from raw log sequences to support incident review

Intelligent Alerting and Incident Enrichment

  • Correlating and deduplicating alerts using semantic analysis to identify related events
  • Automatically gathering contextual information from runbooks, historical incident records, and documentation
  • Routing alerts intelligently based on content analysis and team specialization
  • Mitigating alert fatigue through AI-driven reduction of non-essential notifications

AI-Assisted Root Cause Analysis

  • Generating hypotheses by correlating data from multiple telemetry sources
  • Establishing evidence chains to link symptoms across metrics, logs, and traces
  • Facilitating guided troubleshooting via interactive AI diagnostic sessions
  • Constructing root cause analysis agents capable of progressive investigation for government systems

Automated Incident Response and Communication

  • Creating incident summaries and status updates based on real-time telemetry data
  • Drafting automated postmortem reports with reconstructed event timelines
  • Tailoring stakeholder communications to meet the needs of both technical teams and executive leadership
  • Providing runbook suggestions and automated remediation recommendations to accelerate resolution

ML for Observability

  • Utilizing time-series forecasting for capacity planning and predictive anomaly detection
  • Applying foundation models for zero-shot anomaly detection on system metrics
  • Mapping service dependencies and discovering topology using embedding-based analysis
  • Training and deploying lightweight machine learning models in parallel with observability pipelines

Production Deployment and Ethics

  • Evaluating latency and cost implications for real-time AI observability applications
  • Prioritizing data privacy to ensure sensitive telemetry information is not exposed through LLM interactions
  • Maintaining human oversight, particularly when AI-driven diagnoses require operator validation
  • Assessing operational impact through metrics such as mean time to detect (MTTD), mean time to resolve (MTTR), and on-call personnel experience

Requirements

  • Proficiency with observability platforms including Prometheus, Grafana, Datadog, or OpenTelemetry.
  • Working knowledge of log aggregation and metric analysis principles.
  • Foundational Python scripting capabilities for data manipulation tasks.

Target Participants

  • SRE and observability specialists implementing AI-augmented instrumentation solutions.
  • Platform engineers designing advanced monitoring architectures.
  • DevOps leadership assessing the integration of large language models into incident response procedures.
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories