DevSecOps with AI: Automating Security in the Pipeline Training Course

AI-driven deployment orchestration leverages machine learning and automation to guide 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 deployment pipelines with AI-powered decision-making and resilience capabilities.

Upon completing this training, participants will be able to:

• Deploy AI-assisted rollout strategies for enhanced deployment safety.
• Predict deployment risks using machine learning-driven insights.
• Integrate automated rollback workflows triggered by 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 leverages artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization.

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

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

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

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.

AI-driven rollout control is an approach that applies machine learning, pattern analysis, and adaptive decision models to feature flag operations and canary testing workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and technical leads who wish to improve release reliability and optimize feature exposure decisions using AI-driven analysis.

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

• Apply AI-based decision models to assess the risk of new feature exposure.
• Automate canary analysis using performance, behavioral, and operational indicators.
• Integrate intelligent scoring systems into feature flag platforms.
• Design rollout strategies that dynamically adjust based on real-time data.

Format of the Course

• Guided discussions supported by real-world scenarios.
• Hands-on exercises emphasizing AI-enhanced rollout strategies.
• Practical implementation in 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 utilized to anticipate incidents before they happen and automate root cause analysis (RCA), thereby minimizing downtime and speeding up resolution.

This live training, led by an instructor and available online or on-site, targets advanced IT professionals eager 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 machine learning models to identify patterns that lead to system failures.
• Automate RCA workflows through the correlation of logs and metrics from multiple sources.
• Integrate alerting and remediation processes into existing platforms.
• Deploy and scale intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options

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

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT operations professionals looking to apply AIOps techniques. The goal is to correlate metrics and logs, reduce alert noise, and boost observability through intelligent automation.

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

• For personalized training on this course, please contact us to arrange a session.

By leveraging open-source tools exclusively, organizations can develop cost-efficient and adaptable solutions for monitoring, identifying anomalies, and managing intelligent alerts within production environments.

This instructor-led live training, available online or onsite, targets advanced engineers seeking to construct and implement a complete AIOps pipeline. Participants will utilize tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

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

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 contact us to make arrangements.

AI-driven test generation encompasses a collection of methods and tools designed to 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 to automatically generate tests and predict areas of inadequate coverage.

After completing this workshop, participants will be equipped to:

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

Course Format

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

Course Customization Options

• For training tailored to your specific toolchain or workflows, please contact us to make arrangements.

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

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

Upon completing this training, participants will be able to:

• Generate, prioritize, and maintain tests using AI-powered 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 rapidly evolving agile projects.

Course Format

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

Course Customization Options

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

AI-driven compliance monitoring is a specialized 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) is designed for intermediate-level professionals seeking to integrate AI-powered compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

• Utilize AI-based checks to identify compliance gaps during the software build process.
• Deploy 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 supported 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 tailored compliance integrations, please contact us to arrange.

CI/CD for AI is a structured approach to automating model packaging, testing, containerization, and deployment using continuous integration and continuous delivery pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

As the training concludes, participants will be able to:

• Create automated pipelines for building and testing AI model containers.
• Implement version control and reproducibility for model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices tailored to 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

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

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

This instructor-led live training, available both online and onsite, targets beginner to intermediate professionals aiming to utilize GitHub Copilot to streamline DevOps workflows, enhance automation capabilities, and increase overall productivity.

Upon completing this training, participants will be able to:

• Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipeline creation.
• Apply AI-powered code completion features within YAML files and GitHub Actions.
• Accelerate the execution of testing, deployment, and automation workflows.
• Implement Copilot responsibly, with a clear understanding of its limitations and adherence to best practices.

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 contact us to arrange a tailored program.

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace deliver robust capabilities for detecting anomalies, correlating alerts, and automating responses across extensive IT infrastructures.

This instructor-led, live training (available online or onsite) is designed for intermediate-level enterprise IT teams seeking to integrate AIOps tools into their current observability stacks and operational workflows.

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

• Configure and integrate Splunk, Moogsoft, and Dynatrace into a cohesive 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.
• Enhance performance, reduce MTTR, and boost operational efficiency at an enterprise scale.

Course Format

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

Customization Options

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

Prometheus and Grafana are extensively utilized tools for achieving observability in contemporary infrastructure environments. When augmented with machine learning, these platforms gain the ability to provide predictive and intelligent insights, thereby automating operational decision-making processes.

This instructor-led live training session, available either online or at an onsite location, is designed for intermediate-level observability professionals who aim to modernize their monitoring infrastructure by integrating AIOps practices using Prometheus, Grafana, and machine learning techniques.

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

• Configuring Prometheus and Grafana to ensure comprehensive observability across various systems and services.
• Collecting, storing, and visualizing high-quality time series data.
• Applying machine learning models for the purpose of anomaly detection and forecasting.
• Constructing intelligent alerting rules grounded in predictive insights.

Course Format

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

Course Customization Options

• To request a customized training version of this course, please contact us to make arrangements.

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 by emulating human-like collaboration and decision-making processes.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who aim to design and implement DevOps automation workflows powered 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 agent-driven tools.
• Coordinate multiple agents to triage alerts, generate responses, and provide actionable DevOps recommendations.
• Construct secure and maintainable agent-powered workflows utilizing open-source frameworks.

Course Format

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

Customization Options

• For organizations seeking customized training for this course, please contact us to arrange tailored sessions.

Predictive build optimization leverages machine learning to analyze build behavior, enhancing reliability, speed, and resource utilization.

This instructor-led live training, available online or onsite, is designed for intermediate-level engineering professionals seeking to enhance build pipelines through automation, prediction, and intelligent caching using machine learning techniques.

Upon completing this course, participants will be able to:

• Apply ML techniques to evaluate build performance patterns.
• Detect and predict build failures using historical build logs.
• Implement ML-driven caching strategies to shorten build durations.
• Integrate predictive analytics into existing CI/CD workflows.

Course Format

• Instructor-led lectures and collaborative discussions.
• Practical exercises focused 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 contact us to customize the program.