AIOps Training in Australia

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.

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.

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.

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.

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.

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.

This instructor-led, live training in Australia (online or onsite) is aimed at SRE and DevOps engineers who want to design, build, and safely deploy AI agents for autonomous IT operations.

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.