AI for DevOps Training in Belgrade

AI-driven rollout control is a methodology that leverages machine learning, pattern recognition, and adaptive decision-making models to optimize feature flag operations and canary testing processes.

This instructor-led live training, available online or onsite, targets intermediate engineers and technical leads seeking to enhance release reliability and refine feature exposure decisions through AI-powered analysis.

Upon completing this course, participants will be equipped to:

• Deploy AI-based decision models to evaluate the risks associated with new feature exposure.
• Automate canary analysis by utilizing performance, behavioral, and operational metrics.
• Incorporate intelligent scoring mechanisms into feature flag platforms.
• Develop rollout strategies that dynamically adapt based on real-time data insights.

Course Format

• Guided discussions enriched with real-world scenarios.
• Hands-on exercises focused on AI-enhanced rollout strategies.
• Practical implementation within a simulated feature flag and canary testing environment.

Course Customization Options

• For tailored content or integration of organization-specific tooling, please reach out to us.

Self-healing automation involves utilizing intelligent systems to identify pipeline failures, determine their root causes, and initiate real-time recovery processes.

This instructor-led live training, available both online and onsite, is designed for advanced-level professionals looking to incorporate AI-driven incident detection and automated remediation into their delivery pipelines.

Upon completing this course, participants will be able to:

• Monitor pipelines using AI-powered anomaly detection models.
• Design automated recovery workflows to address failures instantly.
• Implement intelligent feedback loops that prevent recurring issues.
• Enhance overall resilience and reliability within CI/CD systems.

Format of the Course

• Expert-led presentations featuring real-world examples.
• Practical exercises focused on pipeline reliability challenges.
• Hands-on development of automated resolution mechanisms in a lab environment.

Course Customization Options

• For tailored content addressing your organization’s workflows or incident-response needs, please contact us to arrange.

GitHub Copilot is an AI-driven coding assistant designed to automate various development tasks, including DevOps activities such as creating YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led live training (available online or onsite) is intended for beginner to intermediate-level professionals who want to use GitHub Copilot to streamline DevOps tasks, enhance automation, and increase productivity.

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

• Utilize GitHub Copilot to assist with shell scripting, configuration management, and CI/CD pipelines.
• Harness AI code completion for YAML files and GitHub Actions.
• Expedite testing, deployment, and automation workflows.
• Apply Copilot responsibly, with an understanding of AI limitations and best practices.

Course Format

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

Course Customization Options

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

AI-assisted compliance monitoring is a field that leverages intelligent automation to detect, enforce, and validate policy requirements throughout the software delivery lifecycle.

This instructor-led live training (available online or onsite) targets intermediate-level professionals seeking to integrate AI-driven compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

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

Course Format

• Instructor-guided presentations supplemented 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

• For organizations requiring tailored compliance integrations, please contact us to arrange.

CI/CD for AI provides a structured methodology for automating the packaging, testing, containerization, and deployment of models through continuous integration and delivery pipelines.

This instructor-led training, available online or onsite, is designed for intermediate professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

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

• Establish automated pipelines for constructing and testing AI model containers.
• Implement version control and reproducibility measures throughout the model lifecycle.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored to machine learning operations.

Course Format

• Instructor-guided presentations and technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations in a controlled environment.

Course Customization Options

• If your organization requires customized pipeline workflows or specific platform integrations, please contact us to tailor this course.

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

This instructor-led, live training (available online or onsite) is designed for advanced professionals seeking to apply AI techniques for automatic test generation and forecasting areas of insufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilize AI models to produce effective unit, integration, and end-to-end test scenarios.
• Analyze codebases with machine learning to identify potential coverage blind spots.
• Incorporate AI-based test generation into CI/CD workflows.
• Refine test strategies using predictive failure analytics.

Course Format

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

Course Customization Options

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

Predictive build optimization involves leveraging machine learning to examine build behaviors, thereby enhancing system reliability, execution speed, and resource efficiency.

This guided, live training session (available online or on-site) is designed for engineering professionals with intermediate expertise who aim to enhance their build pipelines through automation, predictive capabilities, and intelligent caching mechanisms powered by machine learning.

After completing this course, participants will be capable of:

• Utilizing machine learning methods to evaluate build performance trends.
• Identifying and forecasting build failures by analyzing historical log data.
• Deploying machine learning-based caching strategies to minimize build times.
• Incorporating predictive analytics into current CI/CD workflows.

Course Format

• Instructor-led lectures combined with interactive group discussions.
• Practical exercises centered on the analysis and modeling of build data.
• Direct implementation activities within a simulated CI/CD environment.

Customization Opportunities

• To tailor this training to your specific toolchains or technical environments, please reach out to us for program customization.

Developing an AIOps pipeline exclusively with open-source tools enables teams to create flexible and cost-efficient solutions for production observability, anomaly detection, and intelligent alerting.

This instructor-led live training, available online or onsite, targets advanced engineers seeking to design and deploy a comprehensive end-to-end AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

Upon completion of this training, participants will be capable of:

• Designing an AIOps architecture reliant solely on open-source components.
• Gathering and standardizing data from logs, metrics, and traces.
• Implementing ML models to identify anomalies and forecast incidents.
• Automating alerting and remediation processes using open tooling.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical applications.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For inquiries regarding customized training for this course, please contact us to arrange details.

AI-driven QA automation elevates traditional testing by intelligently generating test cases, optimizing regression coverage, and embedding smart quality gates into CI/CD pipelines, enabling scalable and dependable software delivery.

This instructor-led training, available online or onsite, targets intermediate QA and DevOps professionals looking to leverage AI tools to automate and expand quality assurance within continuous integration and deployment workflows.

Upon completing this training, participants will be equipped to:

• Create, prioritize, and manage tests using AI-powered automation platforms.
• Embed intelligent QA gates into CI/CD pipelines to prevent regressions.
• Apply AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Optimize testing duration and coverage across rapidly evolving agile projects.

Course Format

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

Course Customization Options

• For a customized training version, please contact us to make arrangements.

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 on-site, is designed for intermediate-level enterprise IT teams seeking to integrate AIOps tools into their existing observability stacks and operational workflows.

Upon completing 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, prioritization, and response through built-in and custom workflows.
• Optimize performance, reduce MTTR, and enhance operational efficiency at an enterprise scale.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation within a live-lab environment.

Customization Options

• To request customized training for this course, please contact us to make arrangements.

LLMs and autonomous agent frameworks like AutoGen and CrewAI are redefining how DevOps teams automate tasks such as change tracking, test generation, and alert triage by simulating human-like collaboration and decision-making.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to design and implement DevOps automation workflows powered by large language models (LLMs) and multi-agent systems.

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

• Integrate LLM-based agents into CI/CD workflows for smart automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents for triaging alerts, generating responses, and providing DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

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 gaining traction for its ability to forecast incidents ahead of time and automate Root Cause Analysis (RCA), thereby reducing downtime and speeding up resolution times.

This instructor-led live training, available online or onsite, targets advanced IT professionals eager to apply predictive analytics, automate remediation steps, and design intelligent RCA workflows leveraging AIOps tools and machine learning models.

Upon completing this training, participants will be capable of:

• Constructing and training machine learning models to identify patterns indicative of system failures.
• Automating RCA workflows through the correlation of logs and metrics from multiple sources.
• Integrating alerting and remediation procedures into current platforms.
• Deploying and scaling intelligent AIOps pipelines within production environments.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Customization Options

• For customized training requests, please contact us to make arrangements.

DevSecOps with AI involves integrating artificial intelligence into DevOps pipelines to proactively identify vulnerabilities, enforce security policies, and automate response actions throughout the software delivery lifecycle.

This instructor-led live training (available online or onsite) is designed for intermediate-level DevOps and security professionals who want to leverage AI-based tools and practices to enhance security automation across development and deployment pipelines.

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

• Integrate AI-driven security tools into CI/CD pipelines.
• Leverage AI-powered static and dynamic analysis to detect issues earlier.
• Automate secrets detection, code vulnerability scanning, and dependency risk analysis.
• Enable proactive threat modeling and policy enforcement using intelligent techniques.

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practice sessions.
• 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 deployment orchestration leverages machine learning and automation to guide rollout strategies, detect anomalies, and initiate automatic rollbacks when necessary.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals aiming 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.

Prometheus and Grafana are extensively utilized tools for ensuring observability in contemporary infrastructure, while machine learning augments these platforms by providing predictive and intelligent insights to automate operational decisions.

This instructor-led training session (available online or onsite) targets intermediate-level observability professionals seeking to modernize their monitoring infrastructure by incorporating AIOps methodologies using Prometheus, Grafana, and machine learning techniques.

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

• Configure Prometheus and Grafana to achieve comprehensive observability across various systems and services.
• Gather, store, and visualize high-fidelity time series data.
• Utilize machine learning models for anomaly detection and forecasting.
• Develop intelligent alerting rules grounded in predictive insights.

Course Format

• Engaging lectures and interactive discussions.
• Numerous exercises and practical applications.
• Practical implementation within a live-lab environment.

Customization Options

• For inquiries regarding customized training for this course, please contact us to make arrangements.

AI for DevOps leverages artificial intelligence to elevate continuous integration, testing, deployment, and delivery processes through intelligent automation and advanced optimization strategies.

This instructor-led training, available online or on-site, is designed for intermediate DevOps professionals looking to embed AI and machine learning capabilities into their CI/CD pipelines to boost speed, precision, and overall quality.

Upon completing this training, participants will be equipped to:

• Embed AI tools into CI/CD workflows for intelligent automation.
• Utilize AI for testing, code analysis, and detecting the impact of changes.
• Refine build and deployment strategies using predictive insights.
• Establish traceability and foster continuous improvement through AI-driven feedback loops.

Course Format

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

Customization Options

• For personalized training on this topic, please contact us to make arrangements.

AIOps (Artificial Intelligence for IT Operations) is a practice that uses machine learning and analytics to automate and enhance IT operations, with a particular focus on monitoring, incident detection, and response.

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT operations professionals who want 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.