AI Observability & Monitoringin Edinburgh

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Explore AI Observability & Monitoring

Explore StepMerit's AI Observability & Monitoring course to learn how artificial intelligence enhances system monitoring, application performance analysis, anomaly detection, and incident investigation. Develop practical knowledge of logs, metrics, distributed traces, intelligent alerting, and predictive analytics to identify potential issues and improve system reliability. Understand AI-driven observability practices for modern cloud and application environments through industry-focused learning and practical exercises in Edinburgh, United Kingdom.

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Learning from Edinburgh

Edinburgh offers a strong professional community, cultural heritage and accessible learning opportunities.

Course curriculum

Module 1: Introduction to AI Observability & Monitoring
- Understand the fundamentals of observability, monitoring, and AI-driven operational analysis.
- Explore the role of artificial intelligence in modern IT infrastructure and application management.
- Identify the differences between traditional monitoring and AI-assisted observability.
Module 2: Fundamentals of Monitoring and Observability
- Learn the three pillars of observability: logs, metrics, and distributed traces.
- Understand dashboards, telemetry collection, alerts, and system health indicators.
- Explore monitoring strategies for applications, infrastructure, and business services.
Module 3: AI and Machine Learning Fundamentals
- Understand essential AI and machine learning concepts relevant to observability.
- Explore pattern recognition, anomaly detection, classification, and predictive analytics.
- Identify suitable use cases for AI-assisted monitoring and operational decision-making.
Module 4: Logs, Metrics, and Distributed Tracing
- Learn how logs record events, metrics measure system behaviour, and traces follow requests across services.
- Explore log parsing, metric aggregation, trace analysis, and telemetry correlation.
- Understand how combined telemetry data supports application troubleshooting.
Module 5: Application Performance Monitoring
- Examine response time, latency, throughput, error rates, and service availability.
- Identify performance bottlenecks and resource-related application issues.
- Explore AI-assisted analysis of application performance trends.
Module 6: AI-Based Anomaly Detection
- Understand normal and abnormal system behaviour using operational telemetry.
- Explore statistical methods and machine learning approaches for detecting anomalies.
- Evaluate false positives, missed detections, and the importance of validating AI-generated findings.
Module 7: Intelligent Alerting and Event Correlation
- Learn how monitoring alerts are generated, grouped, and prioritised.
- Explore event correlation to identify relationships between alerts and service incidents.
- Understand alert noise reduction and escalation strategies for operational teams.
Module 8: Root Cause Analysis and Troubleshooting
- Explore how logs, metrics, traces, and dependency information support incident investigation.
- Understand how AI-assisted analysis can identify potential contributing factors.
- Develop structured troubleshooting workflows and validate findings before taking corrective action.
Module 9: Observability for Cloud-Native Applications
- Understand observability requirements for microservices, containers, APIs, and distributed applications.
- Explore service dependencies, container health, and cross-service telemetry.
- Learn monitoring considerations for cloud and hybrid environments.
Module 10: AI-Assisted Incident Management
- Examine incident detection, classification, prioritisation, and escalation processes.
- Explore AI-assisted incident summaries and operational recommendations.
- Understand human approval, incident documentation, and post-incident review practices.
Module 11: Predictive Analytics and Capacity Planning
- Analyse historical telemetry to identify trends and recurring operational patterns.
- Explore forecasting methods for workload demand, resource utilisation, and performance changes.
- Understand the limitations of predictions and the need for continuous validation.
Module 12: Observability Tools and Platforms
- Understand the capabilities of observability platforms such as Prometheus, Grafana, OpenTelemetry, and cloud-native monitoring services.
- Explore telemetry ingestion, dashboards, query interfaces, and alert configuration.
- Examine how AI capabilities can complement established observability tools, depending on platform support.
Module 13: Automation and Observability Workflows
- Identify repetitive monitoring and reporting tasks suitable for automation.
- Explore automated notifications, incident ticket creation, and workflow integration.
- Understand access controls, approval requirements, and safeguards for automated remediation.
Module 14: Observability Governance, Security, and Data Quality
- Understand telemetry data privacy, retention, access permissions, and sensitive information handling.
- Explore data consistency, instrumentation standards, and monitoring configuration management.
- Examine AI explainability, auditability, and responsible use of operational data.
Module 15: Practical Exercises and Scenario-Based Learning
- Analyse sample logs, metrics, traces, and application performance dashboards.
- Investigate simulated anomalies, correlated alerts, and distributed application issues.
- Develop monitoring recommendations and document findings using evidence from the exercises.
Module 16: Capstone Project and Final Assessment
- Design an AI-assisted observability workflow for a simulated application or cloud environment.
- Demonstrate telemetry analysis, anomaly investigation, alert prioritisation, and reporting.
- Present the proposed solution, validation approach, governance controls, and areas for improvement.

Exam details

Exam Level: Beginner to Intermediate – AI Observability & Monitoring
Exam Type: Online computer-based assessment comprising multiple-choice questions (MCQs) and scenario-based questions to evaluate conceptual understanding and practical decision-making.
Number of Questions: 40 questions.
Exam Duration: 90 minutes.
Passing Score: Proposed minimum of 70% (28 out of 40 questions), subject to confirmation by StepMerit.
Practical Assessment: Scenario-based tasks may require learners to interpret monitoring dashboards, analyse telemetry data, investigate unusual system behaviour, prioritise alerts, and recommend observability improvements.
Evaluation Criteria: Learners are assessed on conceptual knowledge, interpretation of monitoring information, troubleshooting approaches, responsible use of AI-assisted insights, and operational decision-making.
Certification: Eligible learners may receive a StepMerit course-completion certificate after meeting the applicable course and assessment requirements. This is not an official certification issued by a third-party technology provider.
Assessment Preparation: Reviewing course modules, practising telemetry analysis, understanding monitoring metrics, and completing guided troubleshooting exercises can help learners prepare for the assessment.
Training Location: StepMerit offers AI Observability & Monitoring training in Edinburgh, United Kingdom, subject to the available delivery format.

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Who this course is for

Software Developers DevOps Engineers Site Reliability Engineers (SREs) Cloud Engineers Cloud Administrators IT Operations Professionals System Administrators Application Performance Engineers Monitoring and Observability Engineers Infrastructure Engineers Platform Engineers AI and Machine Learning Practitioners Technical Support Engineers Cloud Security Professionals Working Professionals seeking AI Observability & Monitoring training in Edinburgh, United Kingdom

Before you begin

Basic IT Fundamentals – Understand essential IT concepts, system architecture, applications, and infrastructure components. Cloud Computing Fundamentals – Familiarity with cloud platforms, deployment models, virtual machines, and cloud services is beneficial. Operating Systems Knowledge – Understand basic Linux or Windows administration, processes, services, and system resource utilisation. Networking Fundamentals – Learn the basics of IP addresses, DNS, HTTP/HTTPS, ports, connectivity, and network troubleshooting. Monitoring and Observability Basics – Familiarity with logs, metrics, traces, dashboards, alerts, and system health monitoring is recommended. Application Performance Concepts –…

Learning outcomes

Problem Statement A financial services organisation operates digital banking applications that handle customer logins, fund transfers, payment processing, account enquiries, and transaction notifications. As transaction volumes increase, the organisation experiences intermittent application latency, unexpected API errors, database performance issues, and a growing number of monitoring alerts. The IT operations team relies heavily on traditional monitoring dashboards and manual log analysis. This makes it difficult to identify abnormal application behaviour, correlate events across interconnected services, and prioritise incidents that could affect customer experience. The organisation needs…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional Experience: 5 years in IT operations, monitoring, cloud technologies, or related technical domains. Training Hours Delivered: 120+ hours of technical training. Candidates Trained: 180+ candidates. Observability and Monitoring: Knowledge of logs, metrics, distributed tracing, dashboards, alerting, and application performance monitoring. AI-Assisted Monitoring: Understanding of anomaly detection, intelligent alerting, pattern recognition, and predictive monitoring. Cloud and Application Monitoring: Familiarity with cloud infrastructure, microservices, distributed applications, and monitoring platforms. Incident Investigation: Knowledge of troubleshooting workflows, alert…

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Insights for AI Observability & Monitoring

Learning insight 01

AI Observability & Monitoring: Improve Application Performance and Reliability

Modern applications operate across cloud platforms, microservices, APIs, databases, and distributed infrastructure. Monitoring these interconnected systems can generate large volumes of logs, metrics, traces, and alerts, making it challenging for IT teams to identify performance issues and investigate incidents…

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