Self-Healing Pipelines: AI for Automated Incident Detection & Recovery Training Course

AI-driven deployment orchestration leverages machine learning and automation to guide rollout strategies, detect anomalies, and trigger automatic rollback when needed.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to optimize deployment pipelines with AI-powered decision-making and resilience capabilities.

Upon completion of this training, participants will be able to:

• Implement AI-assisted rollout strategies for safer deployments.
• Predict deployment risk using machine learning–driven insights.
• Integrate automated rollback workflows based on anomaly detection.
• Enhance observability to support intelligent orchestration.

Format of the Course

• Instructor-led demonstrations with technical deep dives.
• Hands-on scenarios focused on deployment experimentation.
• Practical labs simulating real-world orchestration challenges.

Course Customization Options

• Customized integrations, toolchain support, or workflow alignment can be arranged upon request.

AI for DevOps involves applying artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization techniques.

This instructor-led training (available online or onsite) is designed for intermediate-level DevOps professionals seeking to incorporate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

By the end of this training, participants will be able to:

• Integrate AI tools into CI/CD workflows for intelligent automation.
• Apply AI-based testing, code analysis, and change impact detection.
• Optimise build and deployment strategies using predictive insights.
• Implement traceability and continuous improvement using AI-enhanced feedback loops.

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI-driven rollout control leverages machine learning, pattern analysis, and adaptive decision models to optimise feature flag operations and canary testing workflows.

This instructor-led training, available online or onsite, is designed for intermediate-level engineers and technical leads looking to enhance release reliability and refine feature exposure decisions through AI-driven analysis.

Upon completing this course, participants will be able to:

• Utilise AI-based decision models to evaluate the risk associated with new feature exposure.
• Automate canary analysis using performance, behavioural, and operational indicators.
• Integrate intelligent scoring systems into feature flag platforms.
• Design rollout strategies that dynamically adjust based on real-time data.

Course Format

• Guided discussions supported by real-world scenarios.
• Hands-on exercises emphasising AI-enhanced rollout strategies.
• Practical implementation within a simulated feature flag and canary environment.

Course Customisation Options

• To arrange tailored content or integrate organisation-specific tooling, please contact us.

This instructor-led, live training in Australia (online or onsite) is aimed at observability and SRE engineers who want to integrate LLMs and AI into their monitoring, alerting, and incident analysis workflows.

AIOps (Artificial Intelligence for IT Operations) is increasingly being used to predict incidents before they occur and automate root cause analysis (RCA) to minimize downtime and accelerate resolution.

This instructor-led, live training (online or onsite) is aimed at advanced-level IT professionals who wish to implement predictive analytics, automate remediation, and design intelligent RCA workflows using AIOps tools and machine learning models.

By the end of this training, participants will be able to:

• Build and train ML models to detect patterns leading to system failures.
• Automate RCA workflows based on multi-source log and metric correlation.
• Integrate alerting and remediation processes into existing platforms.
• Deploy and scale intelligent AIOps pipelines in production environments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AIOps (Artificial Intelligence for IT Operations) is a discipline that leverages machine learning and analytics to automate and enhance IT operations, particularly in monitoring, incident detection, and response.

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT operations professionals seeking to implement AIOps techniques to correlate metrics and logs, reduce alert noise, and improve observability through intelligent automation.

By the end of this training, participants will be able to:

• Grasp the principles and architecture of AIOps platforms.
• Correlate data across logs, metrics, and traces to identify root causes.
• Reduce alert fatigue through intelligent filtering and noise suppression.
• Use open-source or commercial tools to monitor and respond to incidents automatically.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Utilising an AIOps pipeline built exclusively from open-source tools enables teams to design cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who aim to build and deploy a comprehensive AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom ML models.

Upon completion of this training, participants will be able to:

• Design an AIOps architecture utilising only open-source components.
• Collect and normalise data from logs, metrics, and traces.
• Apply ML models to detect anomalies and predict incidents.
• Automate alerting and remediation using open tooling.

Course Format

• Interactive lecture and discussion.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a tailored training session for this course, please contact us to arrange.

AI-driven test generation employs techniques and tools that automate the creation of test cases and identify testing gaps through machine learning.

This instructor-led, live training (delivered online or onsite) is designed for advanced professionals aiming to apply AI techniques to automatically generate tests and forecast areas lacking sufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilise AI models to generate effective unit, integration, and end-to-end test scenarios.
• Analyse codebases using machine learning to detect potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimise test strategies based on predictive failure analytics.

Course Format

• Guided technical lectures supported by expert insights.
• Scenario-based practice sessions and hands-on exercises.
• Applied experimentation within a controlled testing environment.

Course Customisation Options

• If you require this training tailored to your specific toolchain or workflows, please contact us to arrange.

AI-powered QA automation elevates traditional testing methodologies by generating intelligent test cases, optimising regression coverage, and embedding smart quality gates within CI/CD pipelines to ensure scalable and reliable software delivery.

This instructor-led, live training (available online or onsite) is designed for intermediate-level QA and DevOps professionals seeking to leverage AI tools to automate and scale quality assurance within continuous integration and deployment workflows.

By the conclusion of this training, participants will be equipped to:

• Generate, prioritise, and maintain tests using AI-driven automation platforms.
• Integrate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilise AI for exploratory testing, defect prediction, and test flakiness analysis.
• Optimise testing time and coverage across fast-paced agile projects.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training version of this course, please contact us to arrange.

This instructor-led, live training in Australia (delivered online or on-site) is designed for SRE and DevOps engineers who wish to design, build, and securely deploy AI agents for autonomous IT operations.

AI-supported compliance monitoring is a discipline that applies intelligent automation to detect, enforce, and validate policy requirements across the software delivery lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to integrate AI-driven compliance controls into their CI/CD pipelines.

After completing this training, attendees will be equipped to:

• Apply AI-based checks to identify compliance gaps during software builds.
• Use intelligent policy engines to enforce regulatory, security, and licensing standards.
• Detect configuration drift and deviations automatically.
• Incorporate real-time compliance reporting into delivery workflows.

Format of the Course

• Instructor-guided presentations supported by practical examples.
• Hands-on exercises focused on real-world CI/CD compliance scenarios.
• Applied experimentation within a controlled DevSecOps lab environment.

Course Customization Options

• If your organisation requires tailored compliance integrations, please contact us to arrange.

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for identifying anomalies, correlating alerts, and automating responses across large-scale IT environments.

This instructor-led, live training (available online or onsite) is designed for intermediate-level enterprise IT teams looking to integrate AIOps tools into their existing observability stacks and operational workflows.

Upon completion of this training, participants will be able to:

• Configure and integrate Splunk, Moogsoft, and Dynatrace into a unified AIOps architecture.
• Correlate metrics, logs, and events across distributed systems using AI-driven analysis.
• Automate incident detection, prioritisation, and response with built-in and custom workflows.
• Optimise performance, reduce MTTR, and improve operational efficiency at enterprise scale.

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

Prometheus and Grafana are widely adopted tools for observability in modern infrastructure, while machine learning enhances these tools with predictive and intelligent insights to automate operations decisions.

This instructor-led, live training (online or onsite) is aimed at intermediate-level observability professionals who wish to modernize their monitoring infrastructure by integrating AIOps practices using Prometheus, Grafana, and ML techniques.

By the end of this training, participants will be able to:

• Configure Prometheus and Grafana for observability across systems and services.
• Collect, store, and visualise high-quality time series data.
• Apply machine learning models for anomaly detection and forecasting.
• Build intelligent alerting rules based on predictive insights.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Australia (online or onsite) is designed for ML engineers and platform teams who need to build robust operational pipelines for LLM-powered applications at scale.

This instructor-led, live training in Australia (online or onsite) is aimed at security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered applications.