CONTACT ONS

Cursusaanbod

The AI Observability Landscape

  • From dashboards to conversations: the shift toward AI-augmented observability
  • LLM capabilities relevant to observability: summarization, reasoning, pattern matching
  • Architecture patterns: embedding AI into existing observability stacks

Natural Language Telemetry Querying

  • Text-to-PromQL: translating natural language into monitoring queries
  • NL querying for Elasticsearch, OpenSearch, and Loki log stores
  • SQL generation 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 with LLMs
  • Anomaly detection in log streams using 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 incident context gathering from runbooks, past incidents, and docs
  • Smart alert routing based on content understanding and team expertise
  • Reducing alert fatigue with 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
  • Building a root cause analysis agent with progressive investigation

Automated Incident Response and Communication

  • Generating incident summaries and status updates from telemetry
  • Automated postmortem drafting with timeline reconstruction
  • Stakeholder communication tailored to technical and executive audiences
  • Runbook suggestion and automated remediation recommendations

ML 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: when AI diagnosis needs operator validation
  • Measuring impact: MTTD, MTTR, and on-call experience metrics

Vereisten

  • Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
  • Familiarity with log management and metrics concepts.
  • Basic Python scripting 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.
 14 Uren

Aangepaste bedrijfsopleiding

Opleidingsoplossingen ontworpen exclusief voor bedrijven.

  • Aangepaste inhoud: We passen de syllabus en praktijkopdrachten aan naar de echte doelen en behoeften van uw project.
  • Voor flexibel schema: Datums en tijden aangepast aan het rooster van uw team.
  • Formaat: Online (live), In-company (bij uw kantoren) of Hybride.
Investering

Prijs per privégroep, online live training, startend vanaf 3200 € + BTW*

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