AIOps Training in Tashkent

An AIOps pipeline built entirely with open-source tools allows teams to design cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting in production environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to build and deploy an end-to-end AIOps pipeline using tools like Prometheus, ELK, Grafana, and custom ML models.

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

• Design an AIOps architecture using 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.

Format of the Course

• Interactive lecture and discussion.
• Lots 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.

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

This instructor-led, live training (available online or on-site) is designed for intermediate-level enterprise IT teams aiming to integrate AIOps tools into their existing observability stack 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 using both built-in and custom workflows.
• Optimise performance, reduce MTTR, and enhance operational efficiency at enterprise scale.

Course Format

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

Course Customisation Options

• To request a customised training session 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 visualize 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 practice that applies machine learning and analytics to automate and improve IT operations, particularly in the areas of monitoring, incident detection, and response.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT operations professionals who wish 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:

• Understand 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.

AIOps is a rapidly evolving field that addresses the needs of modern, complex IT environments—particularly those operating within cloud architectures. The AIOps Foundation course offers a comprehensive introduction to the concepts, technologies, and practices related to the use of artificial intelligence in IT operations.

The program covers the background of AIOps, its core principles, tools, and the organizational challenges faced by IT teams adopting these approaches.

The training concludes with an exam. Passing it grants the globally recognized AIOps Foundation certification, valid for three years.

Who is it for?

This course is designed for professionals and managers involved in:

IT operations

DevOps and Site Reliability Engineering (SRE)

Cloud architecture

Data analysis and Data Science

Software development

IT security

Product and project management