Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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