DevSecOps with AI: Automating Security in the Pipeline Training Course

AI-driven deployment orchestration leverages machine learning and automation to guide rollout strategies, detect anomalies, and trigger automatic rollbacks when necessary.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals seeking to optimize their deployment pipelines through AI-powered decision-making and enhanced resilience.

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.

Course Format

• Instructor-led demonstrations with technical deep dives.
• Hands-on scenarios focused on deployment experimentation.
• Practical labs simulating real-world orchestration challenges.

Customization Options

• Customized integrations, toolchain support, or workflow alignment can be arranged upon request.

AI for DevOps refers to the utilization of artificial intelligence to augment continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization strategies.

This instructor-led live training, available online or onsite, is designed for intermediate-level DevOps professionals seeking to embed AI and machine learning into their CI/CD pipelines to enhance speed, accuracy, and overall quality.

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

• Incorporating AI tools into CI/CD workflows for intelligent automation.
• Employing AI-based testing, code analysis, and change impact detection.
• Refining build and deployment strategies through predictive insights.
• Establishing traceability and continuous improvement via AI-enhanced feedback loops.

Format of the Course

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

Course Customization Options

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

AI-driven rollout control leverages machine learning, pattern analysis, and adaptive decision models to enhance feature flag operations and canary testing workflows.

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

After completing this course, participants will be able to:

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

Course Format

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

Customization Options

• For tailored content or integration of organization-specific tools, please contact us.

AIOps (Artificial Intelligence for IT Operations) is increasingly utilized to anticipate incidents before they happen and to automate root cause analysis (RCA), thereby reducing downtime and speeding up resolution times.

This instructor-led, live training session, available both online and onsite, targets advanced IT professionals looking to implement predictive analytics, automate remediation processes, and design intelligent RCA workflows using AIOps tools and machine learning models.

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

• Developing and training ML models to identify patterns that lead to system failures.
• Automating RCA workflows through the correlation of multi-source logs and metrics.
• Integrating alerting and remediation processes into existing platforms.
• Deploying and scaling intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options for the Course

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

AIOps (Artificial Intelligence for IT Operations) is a discipline that leverages machine learning and advanced 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, targets intermediate-level IT operations professionals looking to apply AIOps techniques. The goal is to correlate metrics and logs, minimize alert noise, and boost observability through intelligent automation.

Upon completing this training, participants will be able to:

• Grasp the core principles and architectural framework of AIOps platforms.
• Correlate data across logs, metrics, and traces to pinpoint root causes.
• Mitigate alert fatigue via intelligent filtering and noise suppression techniques.
• Utilize open-source or commercial tools to automatically monitor and respond to incidents.

Format of the Course

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

Course Customization Options

• For customized training requests, please contact us to arrange.

An AIOps pipeline developed exclusively with open-source tools enables teams to create cost-efficient and adaptable solutions for observability, anomaly detection, and intelligent alerting within production environments.

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

Upon completing this training, participants will be capable of:

• Designing an AIOps architecture comprised entirely of 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-source tooling.

Course Format

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

Customization Options

• To arrange customized training for this course, please contact us to discuss your requirements.

AI-enhanced test generation involves employing techniques and tools that leverage machine learning to automate the creation of test scenarios and anticipate gaps in testing coverage.

This instructor-led, live training (available online or onsite) targets advanced-level professionals seeking to apply AI methodologies for automatic test generation and identifying areas with insufficient coverage.

After completing this workshop, participants will be equipped to:

• Utilize AI models to develop effective unit, integration, and end-to-end test scenarios.
• Examine codebases using machine learning to uncover potential coverage blind spots.
• Incorporate AI-driven 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.

Customization Options

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

AI-powered QA automation elevates traditional testing methodologies by creating intelligent test cases, optimizing regression coverage, and embedding smart quality gates within CI/CD pipelines, ensuring scalable and reliable software delivery.

This instructor-led live training (available online or onsite) is designed for intermediate-level QA and DevOps professionals who want to leverage AI tools to automate and scale quality assurance in continuous integration and deployment workflows.

Upon completing this training, participants will be able to:

• Generate, prioritize, and maintain tests using AI-driven automation platforms.
• Integrate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilize AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Optimize testing time and coverage across fast-paced agile projects.

Course Format

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

Customization Options

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

AI-driven compliance monitoring is a specialized field that utilizes 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 looking to embed AI-powered compliance controls into their CI/CD pipelines.

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

• Implement AI-based checks to uncover compliance gaps during software builds.
• Leverage 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-guided presentations enhanced with practical examples.
• Hands-on exercises centered on real-world CI/CD compliance scenarios.
• Applied experimentation within a controlled DevSecOps lab environment.

Customization Options

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

CI/CD for AI represents a systematic method for automating the packaging, testing, containerization, and deployment of models through continuous integration and continuous delivery pipelines.

This instructor-led, live training, available both online and onsite, targets intermediate-level professionals seeking to streamline end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be equipped to:

• Construct automated pipelines for building and testing AI model containers.
• Establish version control and reproducibility standards throughout the model lifecycle.
• Incorporate automated deployment strategies for AI services.
• Apply 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 in a controlled environment.

Course Customization Options

• Should your organization require customized pipeline workflows or specific platform integrations, please contact us to tailor this course accordingly.

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

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level professionals who wish to use GitHub Copilot to streamline DevOps tasks, improve automation, and boost productivity.

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

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

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.

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 training, available both online and onsite, is designed for intermediate-level enterprise IT teams looking to integrate AIOps tools into their existing observability stack 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 practice opportunities.
• Hands-on implementation in a live-lab environment.

Customization Options

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

Prometheus and Grafana are industry-standard tools for monitoring modern infrastructure, while machine learning augments these platforms with predictive and intelligent insights to automate operational decisions.

This instructor-led live training (available online or onsite) targets intermediate-level observability professionals looking to modernize their monitoring infrastructure by adopting AIOps practices through Prometheus, Grafana, and machine learning techniques.

Upon completing this training, participants will be capable of:

• Configuring Prometheus and Grafana to monitor systems and services effectively.
• Gathering, storing, and visualizing high-fidelity time series data.
• Implementing machine learning models for anomaly detection and predictive forecasting.
• Developing intelligent alerting rules derived from predictive insights.

Course Format

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

Customization Options

• For customized training requests, please reach out to us to arrange the session.

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

This instructor-led live training, available both online and onsite, is designed for advanced engineers seeking to architect and implement DevOps automation workflows driven by Large Language Models (LLMs) and multi-agent systems.

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

• Integrating LLM-based agents into CI/CD pipelines for intelligent automation.
• Automating test generation, commit analysis, and change summaries using agent technologies.
• Coordinating multiple agents to triage alerts, generate responses, and deliver DevOps recommendations.
• Constructing secure and maintainable agent-driven workflows using open-source frameworks.

Course Format

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

Customization Options

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

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

This instructor-led live training, available online or onsite, targets engineering professionals at an intermediate level who seek to enhance their build pipelines via automation, predictive analytics, and intelligent caching techniques powered by machine learning.

After completing this course, participants will be equipped to:

• Utilize machine learning methods to evaluate patterns in build performance.
• Identify and forecast build failures using historical build logs.
• Implement machine learning-driven caching strategies to shorten build times.
• Incorporate predictive analytics into established CI/CD workflows.

Course Format

• Instructor-led lectures accompanied by collaborative discussions.
• Practical exercises centered on analyzing and modeling build data.
• Hands-on implementation within a simulated CI/CD environment.

Customization Options

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