AI for DevOps (AIOps)in Chicago

Module 1: Introduction to AI for DevOps

• Understand the fundamentals of Artificial Intelligence for IT Operations (AIOps).
  • Explore the evolution of DevOps towards intelligent operations.
  • Identify the role of AI and machine learning in modern IT environments.
• Examine business applications of AIOps.
  • Understand common operational challenges addressed by AIOps.
  • Explore practical use cases across cloud infrastructure and enterprise applications.

Module 2: DevOps and IT Operations Fundamentals

• Review core DevOps principles and operational workflows.
  • Understand continuous integration, continuous delivery, and continuous deployment.
  • Explore collaboration between development, operations, and reliability teams.
• Learn essential IT operations concepts.
  • Examine infrastructure management, application availability, and service reliability.
  • Understand incident, problem, and change management processes.

Module 3: Artificial Intelligence and Machine Learning for AIOps

• Explore AI and machine learning concepts relevant to IT operations.
  • Understand supervised and unsupervised learning at a foundational level.
  • Examine classification, clustering, and anomaly detection techniques.
• Understand operational data analysis.
  • Identify patterns and trends in system metrics and event data.
  • Explore the limitations of AI predictions and automated recommendations.

Module 4: Monitoring and Observability

• Understand modern monitoring and observability practices.
  • Explore metrics, logs, traces, and event collection.
  • Learn how telemetry provides visibility into infrastructure and applications.
• Examine observability tools and dashboards.
  • Interpret service health indicators and performance trends.
  • Understand how monitoring data supports operational decisions.

Module 5: Operational Data Collection and Processing

• Learn how operational data is collected and prepared for analysis.
  • Explore data sources from servers, applications, containers, and cloud platforms.
  • Understand data normalisation, filtering, and timestamp alignment.
• Improve data quality for AIOps.
  • Identify missing, duplicated, or inconsistent records.
  • Understand how data quality affects analytical results.

Module 6: AI-Based Anomaly Detection

• Understand anomaly detection in IT environments.
  • Identify unusual changes in CPU, memory, network, and application behaviour.
  • Explore baseline-based and machine-learning-based detection approaches.
• Evaluate anomaly detection results.
  • Distinguish meaningful incidents from normal operational variations.
  • Review false positives and false negatives in monitoring systems.

Module 7: Intelligent Log and Event Analysis

• Apply AI concepts to operational logs and events.
  • Explore log classification, pattern recognition, and event grouping.
  • Understand how natural language processing can assist with log interpretation.
• Improve event analysis workflows.
  • Identify recurring error patterns and unusual event sequences.
  • Organise operational information for further investigation.

Module 8: Intelligent Alert Correlation and Noise Reduction

• Understand alert correlation across IT systems.
  • Examine how related alerts can be grouped into broader incidents.
  • Identify dependencies between infrastructure components and applications.
• Explore alert prioritisation.
  • Classify alerts according to severity, context, and potential business impact.
  • Evaluate approaches for reducing duplicate and low-value notifications.

Module 9: Predictive Analytics for IT Operations

• Explore forecasting techniques for operational planning.
  • Analyse historical performance metrics and workload trends.
  • Understand how forecasting can support capacity and demand planning.
• Apply predictive insights to operational scenarios.
  • Recognise potential resource constraints and performance degradation.
  • Evaluate predictions before making infrastructure decisions.

Module 10: AI-Assisted Incident and Root Cause Analysis

• Understand intelligent incident investigation.
  • Correlate logs, metrics, traces, and events to investigate service disruptions.
  • Explore how AI can suggest possible causes of operational failures.
• Improve incident response practices.
  • Prioritise incidents according to impact and urgency.
  • Validate suggested causes and corrective actions before implementation.

Module 11: AIOps for Cloud and Container Environments

• Explore AIOps applications in cloud infrastructure.
  • Understand operational monitoring across virtual machines, networks, and cloud services.
  • Examine performance and availability indicators in distributed environments.
• Apply AIOps concepts to containers and orchestration.
  • Review monitoring considerations for Docker and Kubernetes environments.
  • Explore resource utilisation, workload health, and container-related events.

Module 12: AI-Driven Automation and Remediation

• Understand automation opportunities in IT operations.
  • Identify repetitive operational tasks suitable for controlled automation.
  • Explore scripts, workflows, and automated response mechanisms.
• Design safe remediation workflows.
  • Define approval steps, execution conditions, and rollback procedures.
  • Validate automated actions in controlled environments before wider deployment.

Module 13: AIOps Integration with DevOps Pipelines

• Explore the relationship between AIOps and CI/CD workflows.
  • Examine how operational feedback can inform build and deployment decisions.
  • Understand the use of monitoring signals during release validation.
• Improve release and deployment visibility.
  • Analyse deployment events and post-release performance indicators.
  • Explore how operational insights can support continuous improvement.

Module 14: Site Reliability Engineering and AIOps

• Understand how AIOps supports reliability engineering.
  • Explore service-level indicators, service-level objectives, and error budgets.
  • Examine availability, latency, and reliability measurements.
• Apply data-driven reliability practices.
  • Identify recurring reliability risks and operational bottlenecks.
  • Use evidence-based analysis to support service improvement decisions.

Module 15: AIOps Security, Governance, and Responsible AI

• Identify security and privacy considerations.
  • Protect sensitive logs, operational records, and infrastructure information.
  • Understand access control and secure handling of monitoring data.
• Apply responsible AI and governance principles.
  • Evaluate model accuracy, explainability, and operational risks.
  • Maintain human oversight, auditability, and approval controls for important actions.

Module 16: Capstone Project and Practical Assessment

• Develop a practical AIOps solution for a simulated IT environment.
  • Collect sample logs, metrics, or event data.
  • Identify anomalies, correlate events, and investigate a simulated incident.
• Implement and evaluate the proposed solution.
  • Demonstrate an alert analysis or operational automation workflow.
  • Document findings, limitations, and recommended improvements.
  • Present the project results with supporting evidence and validation records.

Course Delivery

• Instructor-led sessions covering AIOps principles, tools, and operational use cases.
• Practical demonstrations involving monitoring data, logs, metrics, and incident scenarios.
• Guided exercises, assignments, and case-based learning.
• Capstone project designed to apply AIOps concepts to realistic IT operations challenges.
• StepMerit training and practical learning support in Chicago, United States.