Get in Touch
 Duration 14 hours

Course Outline

Formulating an Open Architecture for Artificial Intelligence Operations

  • Examination of essential structural elements within open AIOps workflows
  • Data transmission pathways from initial ingestion through to alert generation
  • Comparative analysis of tools and strategies for integration

Data Acquisition and Consolidation

  • Processing time-series information via Prometheus
  • Acquisition of system logs using Logstash and Beats
  • Standardizing data structures to facilitate correlation across multiple sources

Development of Observability Interfaces

  • Presentation of performance metrics using Grafana
  • Construction of Kibana interfaces for log analysis
  • Utilization of Elasticsearch queries to derive operational insights

Anomaly Identification and Incident Forecasting

  • Transfer of observability data into Python processing pipelines
  • Calibration of machine learning models for outlier identification and projection
  • Implementation of models for real-time inference within the observability framework

Notification Systems and Automation with Open-Source Solutions

  • Configuration of Prometheus alerting rules and Alertmanager distribution
  • Execution of scripts or API workflows to facilitate automated responses
  • Deployment of open-source orchestration platforms (e.g., Ansible, Rundeck)

Integration and Scalability Factors

  • Management of high-volume data ingestion and extended retention periods
  • Security protocols and access control within open-source environments
  • Independent scaling of system layers: ingestion, processing, and notification

Practical Applications and Expansion

  • Case analyses: performance optimization, outage prevention, and cost reduction
  • Extension of workflows with tracing utilities or service mapping
  • Recommended practices for operating and sustaining AIOps in production settings

Conclusions and Future Directions

Requirements

  • Proficiency with observability platforms such as Prometheus or ELK
  • Practical understanding of Python and machine learning fundamentals
  • Comprehension of IT operations and notification workflows

Intended Audience

  • Senior Site Reliability Engineers (SREs)
  • Data Engineers specializing in operational tasks
  • DevOps Platform Leaders and Infrastructure Architects

Number of participants


Price per participant

Upcoming Courses

Related Categories