About the course
Managing complex IT systems and architectures can feel overwhelming. Our instructor-led AIOps training course offers a high-level, practical understanding of how to leverage Artificial Intelligence for IT Operations. You'll discover the significant benefits of designing and deploying data-driven monitoring and decision-making automation solutions.
This introductory AIOps course covers the fundamentals of data collection, analytics, machine learning, and artificial intelligence. It's designed to give you a solid head-start in building your organisation's automation capabilities, enabling you to proactively manage incidents, predict issues, and optimise performance across your IT estate.
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- Define AIOps and articulate its core concepts, including its role in modern IT operations.
- Understand the principles of data collection, normalisation, and analysis within an AIOps framework.
- Grasp how Machine Learning and Artificial Intelligence are applied to IT operational data.
- Identify and outline the capabilities of popular AIOps tools in the market.
- Determine which operational processes are suitable for automation and understand the key benefits and potential pitfalls.
- Explore AIOps use cases for security, including threat intelligence, network monitoring, and fraud detection.
- Comprehend how AIOps enhances infrastructure performance monitoring and observability, particularly for complex containerised environments.
- Apply AIOps concepts to causal and root cause analysis for efficient troubleshooting.
- Recognise the importance of good logging practices in supporting AIOps initiatives.
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This course is ideal for IT professionals, operations teams, and decision-makers looking to understand and implement AIOps within their organisations. This includes:
IT Operations Managers and Engineers
System Administrators
DevOps and SRE Professionals
Network Engineers
Security Analysts
Cloud Architects
Anyone involved in managing complex IT infrastructure and applications seeking to improve efficiency and reliability through AI.
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You don't need to be an AI expert to join this course, but a foundational understanding of IT operations and infrastructure is beneficial.
Basic familiarity with IT infrastructure concepts (e.g., servers, networks, applications).
An interest in automation and data-driven decision-making.
No prior experience with AI, machine learning, or specific AIOps tools is required.
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This AIOps course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
Online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the globe.
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This course provides a comprehensive introduction to AIOps, covering essential concepts, practical applications, and key considerations for successful implementation.
What is AIOps?
Definitions and core principles.
The evolution from traditional IT monitoring to AIOps.
The role of Big Data, Machine Learning, and AI.
Data Collection & Normalisation
Types of operational data (metrics, logs, traces, events).
Strategies for effective data collection.
The importance of data normalisation and correlation.
Data backup and retention best practices in a Big Data context.
Data Analysis & Intelligence
Overview of analytical techniques in AIOps.
Introduction to Machine Learning concepts relevant to AIOps (e.g., anomaly detection, clustering).
Leveraging Artificial Intelligence for predictive insights and pattern recognition.
Exploring AIOps Tools & Automation
Overview of Popular AIOps Platforms
A look at market-leading solutions: Splunk, AppDynamics (Cisco), Elastic Observability, New Relic One, Datadog, BigPanda, ZIF.
Understanding their unique features and use cases.
Process Automation in AIOps
Identifying operational tasks suitable for automation.
Key benefits of automation, including reduced toil and faster resolution.
Understanding when and when not to automate.
Implementing automation intelligently and incrementally.
Key AIOps Use Cases
Security AIOps
Threat intelligence analysis and correlation.
Network behaviour and endpoint monitoring for anomalies.
AI-driven fraud detection.
Automating responses to security incidents.
Infrastructure Performance Monitoring & Observability
Using AIOps to consolidate and manage diverse monitoring tools.
Monitoring complex containerised architectures (e.g., Kubernetes).
Application performance monitoring (APM) with AIOps.
Predictive maintenance and capacity planning.
Causal Analysis & Troubleshooting
Probable cause analysis: Identifying likely causes of an issue.
Root cause analysis: Deep diving into the ultimate origin of problems.
Leveraging AIOps for faster and more accurate incident resolution.
Good Logging Practices
Designing effective logging strategies for AIOps.
Structured logging and its benefits.
Log aggregation and analysis.
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Here are links to some key AIOps platform and tool providers:
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