Get in Touch

Course Outline

Designing an Open AIOps Architecture

  • Examination of core components within open-source AIOps frameworks
  • Mechanisms for data transmission from collection to alert generation
  • Evaluation of tools and strategies for system integration, tailored for government environments

Data Collection and Aggregation

  • Utilization of Prometheus for the ingestion of time-series telemetry data
  • Log acquisition processes using Logstash and Beats infrastructure
  • Standardization of data formats to facilitate correlation across disparate sources

Building Observability Dashboards

  • Metric visualization capabilities through Grafana interfaces
  • Development of log analytics dashboards using Kibana
  • Application of Elasticsearch queries to derive operational intelligence

Anomaly Detection and Incident Prediction

  • Transmission of observability data to Python-based analytical pipelines
  • Implementation of machine learning models for outlier identification and predictive forecasting
  • Deployment of trained models to support real-time inference within the observability workflow

Alerting and Automation with Open Tools

  • Configuration of Prometheus alert rules and Alertmanager notification routing
  • Execution of automated response mechanisms via scripts and API integrations
  • Deployment of open-source orchestration platforms, such as Ansible and Rundeck

Integration and Scalability Considerations

  • Management of high-volume data ingestion and extended retention policies
  • Implementation of security protocols and access control measures within open-source stacks for government use
  • Independent scaling of system layers, including ingestion, processing, and alerting components

Real-World Applications and Extensions

  • Analysis of case studies focusing on performance optimization, outage mitigation, and fiscal responsibility
  • Expansion of pipeline functionality through distributed tracing and service dependency mapping
  • Establishment of operational best practices for the sustained management of AIOps in production environments

Summary and Next Steps

Requirements

  • Proficiency in utilizing observability platforms, including Prometheus or ELK stack
  • Practical experience with Python programming and core machine learning principles
  • Comprehensive understanding of information technology operations and incident alerting procedures

Audience

  • Senior site reliability engineers (SREs)
  • Data engineers supporting operational functions
  • Leads for DevOps platforms and infrastructure architects
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories