1. Course Description
This course equips participants with knowledge and skills to apply Artificial Intelligence (AI) in IT Operations (AIOps), enabling the automation of system monitoring, anomaly detection, root cause analysis, and resource optimization.
The course combines theoretical foundations with hands-on practice, focusing on real-world scenarios in modern infrastructure environments such as cloud computing, microservices, and DevOps.
2. Learning Outcomes
Upon completion of the course, participants are expected to acquire the following knowledge and skills:
• Understand the concept of AIOps and the role of operational data (logs, metrics, traces) in IT systems management
• Apply AI techniques to system monitoring, anomaly detection, and false alert reduction
• Perform root cause analysis of incidents and predict potential failures prior to their occurrence
• Automate incident response, remediate incidents, and optimize infrastructure costs, particularly in cloud environments
3. Course Structure and Key Modules
Module 1: Overview of AIOps and Operational Data
• AIOps concept: The convergence of Big Data, Machine Learning, and DevOps
• IT data sources: Collection of Logs, Metrics, Traces, and Event data
• Building Data Pipelines: Processing real-time operational data before feeding it into AI models
Module 2: Monitoring and Anomaly Detection
• Dynamic thresholding: Replacing static alerts with AI-driven approaches to reduce alert fatigue
• Detection algorithms: Applying Unsupervised Learning techniques (K-means, Isolation Forest) to identify abnormal system behaviors
• Log analysis using NLP: Applying natural language processing to classify and summarize errors from millions of log entries
Module 3: Root Cause Analysis (RCA)
• Event correlation: Linking isolated alerts to identify the underlying root cause of system issues
• Dependency mapping: Using Graph AI to understand relationships among microservices
• Predictive maintenance: Forecasting hardware failures (e.g., disk failures or memory exhaustion) before incidents occur
Module 4: Automated Response and Incident Remediation
• Self-healing systems: Integrating AI with Ansible/Terraform to automatically restart services or enable auto-scaling
• AI-enabled ChatOps: Building intelligent support bots capable of reporting system status and executing remediation commands via Slack or Microsoft Teams
• Cloud cost optimization: Leveraging AI to analyze usage patterns and recommend optimal server configurations
Module 5: Capstone Project (Practical Implementation)
• Hands-on practice: Building a complete AIOps dashboard using platforms such as ELK Stack, Prometheus, combined with Python/TensorFlow
• Evaluation: Optimizing alert accuracy and reducing MTTR (Mean Time To Repair)
4. Duration: 5 days per class
5. Certification Organization: The International Society of Data Scientists (ISODS)
