AI-Powered QA Automation in CI/CD Training Course

AI-driven deployment orchestration is a method that leverages machine learning and automation to guide deployment strategies, identify anomalies, and initiate automatic rollbacks when necessary.

This instructor-led, live training (available online or on-site) is designed for intermediate-level professionals who aim to enhance their deployment pipelines with AI-powered decision-making and resilience features.

Upon completing this training, participants will be able to:

• Implement AI-assisted rollout strategies for safer deployments.
• Predict deployment risks using insights derived from machine learning.
• Integrate automated rollback processes based on anomaly detection.
• Improve observability to support intelligent orchestration.

Format of the Course

• Instructor-led demonstrations with in-depth technical discussions.
• Hands-on scenarios focusing 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 the application of artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization techniques.

This instructor-led, live training (available online or on-site) is designed for intermediate-level DevOps professionals who want to integrate AI and machine learning into their CI/CD pipelines to improve speed, accuracy, and quality.

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

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

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• 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 is a method that leverages machine learning, pattern analysis, and adaptive decision-making to manage feature flags and canary testing processes.

This instructor-led, live training (available both online and on-site) is designed for intermediate-level engineers and technical leads who want to enhance release reliability and optimize feature exposure decisions through AI-driven insights.

Upon completing this course, participants will be able to:

• Utilize AI-based decision models to evaluate the risk associated with new feature deployments.
• Automate canary analysis by using performance, behavioral, and operational metrics.
• Incorporate intelligent scoring systems into feature flag management platforms.
• Develop rollout strategies that adapt dynamically based on real-time data.

Format of the Course

• Guided discussions supported by practical, real-world scenarios.
• Hands-on exercises focusing on AI-enhanced rollout strategies.
• Practical implementation in a simulated feature flag and canary testing environment.

Course Customization Options

• To tailor the content or integrate specific organizational tools, please contact us.

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

This instructor-led, live training (available online or on-site) is designed for advanced-level IT professionals who wish to implement predictive analytics, automate remediation processes, 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 machine learning models to identify patterns that lead to system failures.
• Automate RCA workflows by correlating logs and metrics from multiple sources.
• Integrate alerting and remediation processes into existing IT platforms.
• Deploy and scale intelligent AIOps pipelines in production environments.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• 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 methodology 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 on-site) 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:

• Grasp the principles and architecture of AIOps platforms.
• Correlate data from logs, metrics, and traces to pinpoint root causes.
• Minimize alert fatigue through smart filtering and noise reduction.
• Utilize open-source or commercial tools for automatic monitoring and incident response.

Format of the Course

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

Course Customization Options

• For a customized training session for this course, please contact us to arrange.

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

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

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

• Design an AIOps architecture leveraging only open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Utilize ML models to identify anomalies and forecast incidents.
• Automate alerting and remediation processes using open tooling.

Format of the Course

• Interactive lectures and discussions.
• Extensive 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 test generation is a set of techniques and tools that automate the creation of test cases and predict testing gaps using machine learning.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who want to apply AI techniques to generate tests automatically and forecast areas with insufficient coverage.

Upon completing this workshop, participants will be prepared to:

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

Format of the Course

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

Course Customization Options

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

AI-supported 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 on-site) is designed for intermediate-level professionals who aim to integrate AI-driven compliance controls into their CI/CD pipelines.

After completing this training, participants will be able to:

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

Format of the Course

• Instructor-guided presentations accompanied 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 organization requires customized compliance integrations, please contact us to arrange.

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

This instructor-led, live training (available both online and onsite) is tailored for professionals at the beginner to intermediate level who are looking to leverage GitHub Copilot to streamline their DevOps tasks, enhance automation, and increase productivity.

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

• Utilize GitHub Copilot for shell scripting, configuration management, and CI/CD pipeline development.
• Take advantage of AI-assisted code completion in YAML files and GitHub Actions.
• Speed up testing, deployment, and automation workflows.
• Use Copilot responsibly by understanding its limitations and best practices.

Format of the Course

• Interactive lectures and discussions.
• Extensive 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.

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

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

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

• Integrate AI-driven security tools into CI/CD pipelines.
• Utilize static and dynamic analysis powered by AI to detect issues at an earlier stage.
• Automate the detection of secrets, code vulnerability scanning, and dependency risk assessment.
• Implement proactive threat modeling and policy enforcement using intelligent methods.

Format of the Course

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

Course Customization Options

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

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

This instructor-led, live training (available online or on-site) is designed for intermediate-level enterprise IT teams who aim to integrate AIOps tools into their current observability stack and operational processes.

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

• Set up and integrate Splunk, Moogsoft, and Dynatrace into a cohesive AIOps architecture.
• Use AI-driven analysis to correlate metrics, logs, and events across distributed systems.
• Automate the detection, prioritization, and response to incidents using both built-in and custom workflows.
• Enhance performance, reduce Mean Time To Resolution (MTTR), and improve operational efficiency at an enterprise level.

Format of the Course

• Interactive lectures and discussions.
• 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.

Prometheus and Grafana are widely used tools for observability in modern infrastructure, while machine learning enhances these tools by providing predictive and intelligent insights to automate operational decisions.

This instructor-led, live training (available both online and onsite) is designed for intermediate-level observability professionals who want to modernize their monitoring systems by integrating AIOps practices using Prometheus, Grafana, and machine learning techniques.

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

• Configure Prometheus and Grafana for comprehensive system and service observability.
• Collect, store, and visualize high-quality time series data effectively.
• Apply machine learning models for anomaly detection and forecasting.
• Create intelligent alerting rules based on predictive insights.

Format of the Course

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

Course Customization Options

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

Large language models (LLMs) and autonomous agent frameworks such as AutoGen and CrewAI are transforming how DevOps teams automate tasks like change tracking, test generation, and alert triage by mimicking human-like collaboration and decision-making processes.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who want to design and implement DevOps automation workflows using 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 intelligent automation.
• Automate test generation, commit analysis, and change summaries using these agents.
• Coordinate multiple agents to triage alerts, generate responses, and provide DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Plenty of 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.

Predictive build optimization involves using machine learning to analyze build behavior and enhance reliability, speed, and resource efficiency.

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineering professionals who want to improve their build pipelines through automation, predictive analytics, and intelligent caching techniques.

Upon completing this course, attendees will be able to:

• Use machine learning methods to evaluate build performance trends.
• Identify and predict build failures by analyzing historical build logs.
• Implement caching strategies driven by machine learning to shorten build times.
• Incorporate predictive analytics into existing CI/CD workflows.

Format of the Course

• Guided lectures and interactive discussions led by an instructor.
• Practical exercises focused on analyzing and modeling build data.
• Hands-on implementation within a simulated CI/CD environment.

Course Customization Options

• To tailor this training to specific toolchains or environments, please contact us for customization options.

Self-healing automation involves the use of intelligent systems to detect pipeline failures, pinpoint root causes, and initiate real-time recovery actions.

This instructor-led, live training (available online or on-site) is designed for advanced professionals who want to integrate AI-driven incident detection and automated remediation into their delivery pipelines.

Upon completing this course, participants will be able to:

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

Format of the Course

• Expert-led presentations with practical real-world examples.
• Applied exercises focusing on pipeline reliability challenges.
• Hands-on development of automated resolution mechanisms in a lab environment.

Course Customization Options

• For customized content that addresses your organization’s specific workflows or incident-response needs, please contact us to arrange.