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.
Course Outline
Designing an Open AIOps Architecture
-
Overview of essential components in open AIOps pipelines.
-
Data flow progression from ingestion to alerting.
-
Tool comparison and integration strategies.
Data Collection and Aggregation
-
Ingesting time-series data via Prometheus.
-
Capturing logs using Logstash and Beats.
-
Normalizing data to facilitate cross-source correlation.
Developing Observability Dashboards
-
Visualizing metrics through Grafana.
-
Constructing Kibana dashboards for log analytics.
-
Utilizing Elasticsearch queries to derive operational insights.
Anomaly Detection and Incident Prediction
-
Exporting observability data into Python pipelines.
-
Training machine learning models for outlier detection and forecasting.
-
Deploying models for real-time inference within the observability pipeline.
Alerting and Automation with Open Tools
-
Establishing Prometheus alert rules and Alertmanager routing.
-
Initiating scripts or API workflows for automated responses.
-
Leveraging open-source orchestration tools (e.g., Ansible, Rundeck).
Integration and Scalability Considerations
-
Managing high-volume ingestion and long-term data retention.
-
Ensuring security and access control within open-source stacks.
-
Independently scaling each layer: ingestion, processing, and alerting.
Real-World Applications and Extensions
-
Case studies covering performance tuning, downtime prevention, and cost optimization.
-
Extending pipelines with tracing tools or service graphs.
-
Best practices for operating and maintaining AIOps in production.
Summary and Next Steps
Requirements
-
Prior experience with observability platforms such as Prometheus or ELK.
-
Proficiency in Python along with a foundational understanding of machine learning.
-
Familiarity with IT operations and alerting workflows.
Target Audience
-
Advanced Site Reliability Engineers (SREs).
-
Data engineers focused on operational support.
-
DevOps platform leads and infrastructure architects.
14 Hours
