AIOps Foundation Training is a comprehensive course designed to provide participants with a strong understanding of Artificial Intelligence for IT Operations (AIOps) concepts and practices. AIOps is a methodology that combines artificial intelligence (AI) and machine learning (ML) technologies with IT operations to enhance the management and monitoring of complex IT environments. This training aims to equip learners with the knowledge and skills to leverage AIOps tools and techniques to automate tasks, predict issues, and optimize IT operations.
Learn technology with a experienced professional who have expertise in their particular technology.
We are providing training based on practical oriented approach which aims to clear all doubts by giving more practice while learning
We are providing job oriented course training which focuses on the knowledge and skills required for the job.
We believe that everyone should get the opportunity to learn their desired course. So we provide flexibility timings.
In live project to Known what, why and how of any application build in that technology.
We offer lifetime access to our learning system which make trainees to gain all the knowledge and skills required for the job.
Introduction to AIOps
Understanding AIOps and its significance
Key concepts in AIOps
AIOps vs. traditional IT operations
Benefits and challenges of implementing AIOps
AIOps tools and technologies overview
Data Collection and Integration
Data sources for AIOps
Log data collection and analysis
Metrics and performance data integration
Tracing and correlation of data
Data quality and normalization
Machine Learning Fundamentals
Introduction to machine learning in AIOps
Supervised vs. unsupervised learning
Feature engineering and selection
Model training and evaluation
Applying machine learning to IT operations
Detecting anomalies and outliers
Threshold-based vs. statistical anomaly detection
Machine learning-based anomaly detection
Root cause analysis for anomalies
False positives and negatives in anomaly detection
Event Correlation and Analysis
Event correlation techniques
Topological and causal event correlation
Pattern recognition for event analysis
Incident management and resolution
Automating event responses
Forecasting and trend analysis
Predictive modeling for capacity planning
Anticipating resource utilization
Predictive maintenance use cases
Evaluation of predictive models
Root Cause Analysis
Identifying root causes of issues
Impact analysis and dependency mapping
Correlation of data across IT infrastructure
Visualizing root cause relationships
Automated root cause determination
Workflow orchestration and automation
Integration with IT service management
Automating incident response
Implementing playbooks and runbooks
Natural language processing (NLP) in AIOps
Sentiment analysis and chatbots
Human-AI collaboration in IT operations
Cognitive insights from unstructured data
Ethical considerations in cognitive intelligence
Performance Monitoring and Optimization
Real-time performance monitoring
Application and infrastructure performance
Optimizing resource allocation
Continuous improvement strategies
Balancing performance and cost
Steps to implement AIOps
Organizational readiness and culture
Data governance and security in AIOps
Selecting and evaluating AIOps tools
Best practices for successful AIOps adoption
Future Trends in AIOps
Emerging technologies in AIOps
AI-driven autonomous operations
The role of AIOps in DevOps and SRE
Challenges and opportunities in AIOps evolution
Preparing for the future of IT operations
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