AI-Powered QA Automation in CI/CD Training Course

AI-driven deployment orchestration employs machine learning and automation to steer rollout strategies, identify anomalies, and initiate automatic rollbacks when necessary.

This instructor-led, live training (available online or onsite) targets intermediate-level professionals seeking to optimize their deployment pipelines using AI-powered decision-making and resilience features.

Upon completing this training, participants will be able to:

• Implement AI-assisted rollout strategies to ensure safer deployments.
• Predict deployment risks by leveraging 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 accompanied by 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 leveraging artificial intelligence to optimize continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization strategies.

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

Upon completion, participants will be able to:

• Incorporate AI tools into CI/CD workflows for intelligent automation.
• Utilize AI for testing, code analysis, and identifying change impacts.
• Refine build and deployment strategies with predictive insights.
• Establish traceability and continuous improvement via AI-enhanced feedback loops.

Course Format

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

Customization Options

• For customized training arrangements, please contact us.

AI-driven rollout control applies machine learning, pattern analysis, and adaptive decision models to the management of feature flags and canary testing workflows.

This instructor-led, live training (available online or onsite) targets intermediate-level engineers and technical leads who aim to enhance release reliability and optimize feature exposure decisions through AI-driven analysis.

Upon completing this course, participants will be able to:

• Utilize AI-based decision models to evaluate the risk associated with exposing new features.
• Automate canary analysis by leveraging performance, behavioral, and operational indicators.
• Incorporate intelligent scoring systems into feature flag platforms.
• Develop rollout strategies that dynamically adjust in response to real-time data.

Format of the Course

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

Course Customization Options

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

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) represents a methodology that leverages machine learning and advanced analytics to automate and enhance IT operations, with a focus on monitoring, detecting incidents, and responding to them.

This instructor-led, live training (available online or onsite) is designed for IT operations professionals at an intermediate level who aim to apply AIOps techniques to correlate metrics and logs, minimize alert noise, and enhance observability through intelligent automation.

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

• Grasp the foundational principles and architecture of AIOps platforms.
• Correlate data from logs, metrics, and traces to pinpoint root causes.
• Alleviate alert fatigue via intelligent filtering and noise suppression.
• Utilize open-source or commercial tools to automatically monitor and respond to incidents.

Course Format

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

Course Customization Options

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

Leveraging entirely open-source tools to build an AIOps pipeline enables teams to develop cost-efficient and adaptable solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led live training, available either online or onsite, is designed for advanced engineers aiming to construct and deploy a comprehensive AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Architecting an AIOps setup using exclusively 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.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Customization Options for the Course

• To arrange customized training for this course, please reach out to us.

AI-enhanced test generation encompasses a range of methodologies and tools that utilize machine learning to automate test case creation and identify areas lacking sufficient testing coverage.

This instructor-led training session, available both online and in-person, is designed for advanced professionals looking to implement AI techniques to automatically produce tests and predict gaps in coverage.

After finishing this workshop, participants will be equipped to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Employ machine learning to analyze codebases and identify potential areas of insufficient coverage.
• Incorporate AI-based test generation into CI/CD pipelines.
• Refine testing strategies using predictive failure analytics.

Course Format

• Technical lectures guided by expert insights.
• Hands-on exercises and practice sessions based on real-world scenarios.
• Practical experimentation within a controlled testing environment.

Customization Options

• If you require this training to be customized for your specific tools or workflows, please reach out to us to make arrangements.

AI-assisted compliance monitoring represents a specialized field that leverages intelligent automation to identify, enforce, and verify policy requirements throughout the entire software delivery lifecycle.

This instructor-led, live training session (available online or on-site) is designed for intermediate-level professionals aiming to embed AI-driven compliance controls into their CI/CD pipelines.

Upon completion of this training, participants will gain the ability to:

• Implement AI-driven assessments to uncover compliance discrepancies during software build processes.
• Utilize intelligent policy engines to enforce regulatory, security, and licensing standards.
• Automatically identify configuration drift and deviations.
• Integrate real-time compliance reporting into delivery workflows.

Course Format

• Instructor-led presentations complemented by practical examples.
• Practical exercises centered on real-world CI/CD compliance scenarios.
• Applied experimentation conducted within a controlled DevSecOps lab environment.

Customization Options for the Course

• If your organization requires customized compliance integrations, please contact us to make arrangements.

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

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

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

• Establishing automated pipelines for the construction and testing of AI model containers.
• Enforcing version control and reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices specifically adapted for machine learning operations.

Format of the Course

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

Course Customization Options

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

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

This instructor-led live training, available both online and onsite, targets beginner to intermediate-level professionals aiming to leverage GitHub Copilot to streamline DevOps tasks, enhance automation, and increase productivity.

Upon completing this training, participants will be able to:

• Utilize GitHub Copilot to assist with shell scripting, configuration, and CI/CD pipelines.
• Leverage AI code completion within YAML files and GitHub Actions.
• Accelerate testing, deployment, and automation workflows.
• Apply Copilot responsibly, understanding its limitations and best practices.

Format of the Course

• Interactive lecture and discussion.
• Abundant exercises and practice opportunities.
• 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 incorporating artificial intelligence into DevOps workflows to proactively identify vulnerabilities, enforce security standards, and automate remediation actions throughout the software delivery lifecycle.

This instructor-led training, available online or on-site, is designed for intermediate DevOps and security professionals looking to leverage AI-driven tools and methodologies to strengthen security automation within development and deployment pipelines.

Upon completing this training, participants will be capable of:

• Integrating AI-enhanced security tools into CI/CD pipelines.
• Utilizing AI-powered static and dynamic analysis to identify issues earlier in the process.
• Automating the detection of secrets, code vulnerability scanning, and dependency risk analysis.
• Implementing proactive threat modeling and policy enforcement through intelligent techniques.

Course Format

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

Course Customization Options

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

Enterprise-grade AIOps solutions such as Splunk, Moogsoft, and Dynatrace offer robust features for identifying anomalies, linking alerts, and automating remediation actions across extensive IT infrastructures.

This instructor-led live training, available online or in person, targets intermediate-level IT teams within enterprises seeking to incorporate AIOps tools into their current observability frameworks and operational processes.

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

• Set up and integrate Splunk, Moogsoft, and Dynatrace into a cohesive AIOps architecture.
• Correlate metrics, logs, and events across distributed systems leveraging AI-powered analysis.
• Automate incident identification, prioritization, and resolution using standard and tailored workflows.
• Enhance system performance, decrease MTTR, and boost operational efficiency at an enterprise level.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Practical implementation within a live lab environment.

Customization Options

• For inquiries regarding custom training for this course, please reach out to us to make arrangements.

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.

Large Language Models (LLMs) and autonomous agent frameworks, such as AutoGen and CrewAI, are transforming the way DevOps teams automate tasks like change tracking, test generation, and alert triage by emulating human-like collaboration and decision-making processes.

This instructor-led live training, available online or on-site, is designed for advanced engineers who want to design and implement DevOps automation workflows driven by large language models (LLMs) and multi-agent systems.

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

• Integrate LLM-based agents into CI/CD workflows to enable intelligent automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents for alert triage, response generation, and DevOps recommendations.
• Construct secure and maintainable agent-powered workflows utilizing open-source frameworks.

Course Format

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

Customization Options

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

Proactive build optimization involves leveraging machine learning to evaluate build behaviors, thereby enhancing reliability, execution speed, and resource efficiency.

This guided live training (available online or in-person) is designed for engineering professionals at an intermediate level who aim to enhance their build pipelines by implementing automation, predictive capabilities, and smart caching through machine learning methods.

After completing this course, participants will be able to:

• Utilize ML techniques to evaluate patterns in build performance.
• Identify and forecast build failures using historical build logs.
• Deploy ML-driven caching strategies to minimize build times.
• Incorporate predictive analytics into current CI/CD workflows.

Course Format

• Guided lectures by the instructor combined with collaborative discussions.
• Practical exercises centered on analyzing and modeling build data.
• Practical implementation within a simulated CI/CD environment.

Customization Opportunities

• To tailor this training to your specific toolchains or environments, please reach out to us to customize the program.