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AIOps Certification Training
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About Course
🌐 AIOps Online Training
AIOps (Artificial Intelligence for IT Operations) is an advanced approach that uses AI, machine learning, and big data analytics to automate and enhance IT operations. It helps organizations monitor systems, detect anomalies, predict issues, and resolve incidents faster. With the growing complexity of IT infrastructure and cloud environments, professionals skilled in AIOps are in high demand for roles in DevOps, Site Reliability Engineering (SRE), and IT operations.
This course provides in-depth, hands-on experience in implementing AIOps solutions, analyzing system data, automating workflows, and improving operational efficiency using modern tools and techniques.
🚀 Its Core Capabilities Include:
Data Collection & Monitoring:
Collect and analyze logs, metrics, and events from multiple systems.
AI & Machine Learning:
Apply ML algorithms for anomaly detection and predictive analysis.
Event Correlation:
Identify patterns and reduce noise by correlating multiple alerts.
Incident Management:
Automate incident detection, root cause analysis, and resolution.
Predictive Analytics:
Forecast potential issues before they impact systems.
Automation & Remediation:
Automate repetitive tasks and self-healing processes.
Cloud & DevOps Integration:
Integrate AIOps with cloud platforms and CI/CD pipelines.
📍 Bonus: Certification Tracks
- DevOps Associate Certification
- AIOps Developer Certification
- AIOps Application Developer Certification
- AIOps Expert Certification (Viswa Online Trainings)
AIOps Training Course Syllabus
Chapter 1 – Introduction to AIOps
- What is AIOps
- Evolution of IT Operations
- Why AIOps is needed
- Difference between Traditional IT Ops and AIOps
- Core architecture of AIOps platforms
- Business benefits and ROI
Chapter 2 – IT Operations Fundamentals
- Basics of IT infrastructure (Servers, Network, Cloud)
- DevOps and SRE concepts
- Incident, Problem, and Change management
- ITSM fundamentals
- SLAs, SLOs, and KPIs
Chapter 3 – Data in AIOps
- Types of data: Logs, Metrics, Events, Traces
- Telemetry and observability concepts
- Data collection and ingestion
- Data preprocessing and normalization
- Real-time vs batch processing
Chapter 4 – Big Data Technologies
- Distributed systems basics
- Data storage systems
- Stream processing concepts
- Message queues and event streaming
- Data pipelines architecture
Chapter 5 – Machine Learning for AIOps
- Introduction to Machine Learning
- Supervised learning
- Unsupervised learning
- Time series forecasting
- Anomaly detection techniques
- Model evaluation and tuning
Chapter 6 – AI Techniques in Operations
- Root Cause Analysis using ML
- Event correlation
- Pattern recognition
- Predictive maintenance
- Capacity forecasting
Chapter 7 – Observability and Monitoring
- Monitoring vs Observability
- Alert management
- Noise reduction techniques
- Distributed tracing
- Performance monitoring
Chapter 8 – Automation and Orchestration
- Runbook automation
- Workflow orchestration
- Infrastructure as Code
- CI/CD integration
- Self-healing systems
Chapter 9 – AIOps Tools and Platforms
- Log management tools
- Monitoring dashboards
- Cloud monitoring tools
- ML platforms
- Automation tools
Chapter 10 – Cloud and AIOps
- AIOps in cloud environments
- Multi-cloud monitoring
- Kubernetes monitoring basics
- Container observability
Chapter 11 – Security and Governance
- Security in AI systems
- Risk management
- Compliance and governance
- Ethical AI considerations
Chapter 12 – Implementation Strategy
- AIOps adoption roadmap
- Integration with existing systems
- Organizational change management
- Measuring success metrics
Chapter 13 – Real-world Use Cases
- Intelligent incident management
- Automated ticket resolution
- Predictive outage detection
- Capacity optimization
- Business impact analysis
Chapter 14 – Capstone Project
- Designing an AIOps architecture
- Building anomaly detection model
- Creating monitoring dashboard
- Implementing automated remediation
AIOps Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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Live Virtual Training
- Schedule your sessions at your comfortable with timings
- Instructor-Led training real time projects
- Certification Guidance
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- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
- Get access for lifetime.
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- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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AIOps Online Training FAQ'S
What is AIOps?
AIOps uses AI and machine learning to automate IT operations, monitor systems, and improve incident management.
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What are the key components of AIOps?
- Data collection
- Machine learning
- Event correlation
- Automation
What is anomaly detection in AIOps?
- Anomaly detection identifies unusual patterns in system behavior that may indicate potential issues.
How does AIOps help in incident management?
AIOps detects issues early, identifies root causes, and automates resolution to reduce downtime.
What is event correlation?
Event correlation groups related alerts to identify the root cause and reduce alert noise.
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