AiOps Certification Cum Training Program for 2025, modeled on the thorough, modern, and hands-on approach you established for MLOps, but now focused on the full lifecycle of AiOps—the intersection of AI, IT operations, automation, and observability.

Below you’ll find:

• What AiOps is and why it matters
• The most relevant skill domains and tools
• A complete, modern, and industry-ready curriculum structure
• Rationale for each section, plus recommendations for real-world labs/capstone projects

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What Is AiOps and Why Does It Matter?

AiOps (Artificial Intelligence for IT Operations) is the discipline of applying AI/ML and data analytics to automate, enhance, and optimize IT operations.
The goal: predict, prevent, and resolve incidents faster, reduce noise, improve uptime, and enable self-healing systems.

AiOps engineers must be fluent in:

• Machine learning
• IT operations and SRE principles
• Observability (metrics, logs, traces)
• Automation and orchestration
• Incident management
• Cloud-native platforms

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AiOps Certification Cum Training Program (2025)

By AiOpsSchool.com

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1. Foundations: DevOps, SRE, and AiOps Concepts

• DevOps Concepts
  (Automation, CI/CD, Infrastructure as Code, version control)
• Site Reliability Engineering (SRE) Principles
  (SLI/SLO/SLA, error budgets, toil reduction, incident response)
• AiOps Overview & Industry Use Cases
  (Root cause analysis, event correlation, predictive alerting, intelligent automation)

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2. Infrastructure & Cloud Skills

• Linux and Bash Scripting
• Cloud Platforms: AWS, Azure, GCP Overview
  (Multi-cloud basics for monitoring & automation)
• Containers: Docker Essentials
• Orchestration: Kubernetes Basics

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3. Data Engineering for AiOps

• Data Collection from IT Systems
  (APIs, log scraping, syslog, SNMP, Prometheus exporters)
• Data Integration and ETL Pipelines
  (Apache NiFi or Airflow for log and metric pipelines)
• Streaming Data Processing
  (Apache Kafka, AWS Kinesis basics)

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4. Observability & Monitoring

• Metrics: Prometheus, CloudWatch, DataDog
• Logs: ELK Stack (Elasticsearch, Logstash, Kibana), Graylog, Loki
• Traces: Jaeger, OpenTelemetry
• Alerting & Dashboards: Grafana, Kibana

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5. Event Correlation and Incident Management

• Event Aggregation Platforms
  (Moogsoft, BigPanda, Splunk On-Call, PagerDuty intro)
• Intelligent Alerting & Noise Reduction
  (Anomaly detection, deduplication with AI)
• Incident Response Automation
  (Automated ticketing, runbook automation, ChatOps)

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6. AI/ML for IT Operations

• ML Basics for Time Series & Anomaly Detection
  (Forecasting, trend analysis, outlier detection with scikit-learn, Prophet, PyCaret)
• Deep Learning for IT Ops
  (RNN/LSTM for log and metric anomaly detection)
• Natural Language Processing for Logs and Tickets
  (Log clustering, intent recognition, automated ticket classification)
• Event Correlation with ML
  (Root cause analysis using clustering/graph-based AI)

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7. Automation & Remediation

• Runbook Automation: StackStorm, Rundeck
• Remediation Scripting: Python, PowerShell
• Self-Healing Infrastructure Concepts
• Integration with ITSM (ServiceNow, Jira Service Management basics)

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8. AIOps Platform Engineering

• AIOps Toolchains Overview:
  (Moogsoft, BigPanda, IBM Watson AIOps, Splunk, ServiceNow AIOps, Dynatrace, NewRelic AI, Elastic AI, etc.)
• Open Source AIOps Frameworks
  (OpenAIOps, Prometheus+ML, custom pipelines)
• AIOps Pipelines Design
  (Data ingestion → analytics → correlation → automation)

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9. Security Operations with AI

• SOAR (Security Orchestration, Automation & Response) Fundamentals
  (Demisto, Splunk Phantom intro)
• SIEM with AI Enhancements
  (Elastic SIEM, IBM QRadar, Azure Sentinel with AI modules)

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10. Governance, Compliance, and Ethics in AIOps

• Data Privacy & Compliance
  (GDPR, HIPAA, SOC2 for ops data)
• AI Model Governance
  (Drift detection, bias monitoring, reproducibility)
• Ethics in Automated Ops
  (Transparency, explainability, trust)

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11. Project Management and Collaboration

• Agile/Scrum for AIOps
• Documentation: Confluence
• Collaboration: Slack, Teams, ChatOps (Bot Integration)

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12. Capstone Projects & Hands-On Labs

• AIOps Mini-Project:
  Build a pipeline to collect and analyze system logs/metrics, detect anomalies, and trigger auto-remediation.
• Incident Management Scenario:
  Simulate incident storms, event correlation, noise reduction, and automated ticketing.
• Root Cause Analysis with ML:
  Cluster historical incidents, identify patterns, and build a recommendation system for incident response.
• AIOps Platform Comparison Lab:
  Evaluate at least one commercial and one open source AIOps tool.

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Bonus (Optional Advanced Modules)

• GenAI for IT Operations:
  (Use LLMs for ticket summarization, knowledge base search, chatbots for ops)
• Edge AIOps:
  (AIOps for IoT/Edge, lightweight monitoring/automation)
• Cost Optimization with AI
  (Predictive autoscaling, cloud cost anomaly detection)

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AiOps Certification Program Structure

Module	Core Topics	Tools/Platforms	Hands-On Labs/Projects
1. Foundations	DevOps, SRE, AiOps	Slides, Jira, Git	Quiz, Case Studies
2. Infra & Cloud	Linux, Cloud, K8s	AWS, GCP, Docker	Cloud setup lab
3. Data Eng.	ETL, Streaming	Airflow, NiFi, Kafka	Data pipeline lab
4. Observability	Metrics, Logs, Traces	Prometheus, ELK, Grafana, Jaeger	Monitoring dashboard
5. Events/Incidents	Aggregation, Incident Mgmt	Moogsoft, PagerDuty	Event storm simulation
6. ML for IT Ops	Anomaly, Root Cause	scikit-learn, Prophet	Anomaly detection notebook
7. Automation	Runbooks, Remediation	StackStorm, Rundeck	Auto-remediation demo
8. AIOps Tools	Platforms, Frameworks	BigPanda, Splunk, OpenAIOps	Tool comparison
9. Security	SOAR, SIEM, AI	Demisto, Elastic SIEM	SOC automation case
10. Governance	Privacy, Model Mgmt	Custom/lectures	Ethics case study
11. PM/Collab	Agile, Docs	Confluence, Slack	Team project
12. Capstone	Real-world Project	All above	Full AIOps pipeline

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Why This Is the Best AIOps Certification Program in the World

• Covers the entire AiOps lifecycle: From infra and data engineering to machine learning, automation, incident management, security, and compliance.
• Hands-on with leading commercial and open-source tools.
• Focus on real industry use cases and project-based learning.
• Multi-cloud and hybrid-ready skills.
• Forward-looking (GenAI, edge, cost optimization, security).
• Collaboration, project management, and communication skills included.
• Capstone projects simulate actual enterprise challenges.

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