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 rollback when needed.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish 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.

AI for DevOps refers to the application of artificial intelligence to enhance 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 looking to integrate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

Upon completing this training, participants will be able to:

• Incorporate 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.
• Numerous 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.

AI-driven rollout control employs 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) is designed for intermediate-level engineers and technical leads aiming to boost 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 new feature exposure.
• Automate canary analysis using performance, behavioral, and operational metrics.
• Incorporate intelligent scoring systems into feature flag platforms.
• Develop rollout strategies that dynamically adjust in response to real-time data.

Course Format

• Guided discussions supported by real-world scenarios.
• Hands-on exercises focusing on 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 utilised to anticipate incidents before they happen and to automate root cause analysis (RCA), thereby minimising downtime and accelerating resolution.

This instructor-led, live training (available online or onsite) is designed for advanced-level IT professionals who wish to implement predictive analytics, automate remediation, and design intelligent RCA workflows using AIOps tools and machine learning models.

Upon completing 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.

Course Format

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

Course Customisation Options

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

AIOps (Artificial Intelligence for IT Operations) is a discipline that leverages machine learning and advanced analytics to automate and enhance IT operations, specifically focusing on monitoring, incident detection, and response capabilities.

This instructor-led live training (available online or onsite) is designed for IT operations professionals at an intermediate level who aim to implement AIOps techniques. The course focuses on correlating metrics and logs, minimising alert noise, and improving observability through intelligent automation.

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

• Grasp the core principles and architecture of AIOps platforms.
• Correlate data across logs, metrics, and traces to identify root causes.
• Mitigate alert fatigue through intelligent filtering and noise suppression.
• Utilise open-source or commercial tools to monitor and respond to incidents automatically.

Course Format

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

Course Customization Options

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

An AIOps pipeline constructed entirely from open-source tools enables teams to create cost-efficient and adaptable solutions for monitoring, anomaly detection, and intelligent alerting within production environments.

This instructor-led training session, available online or onsite, 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 completion of this training, participants will be equipped to:

• Architect an AIOps solution using exclusively open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Implement machine learning models to identify anomalies and forecast incidents.
• Automate alerting processes and remediation actions using open tooling.

Course Structure

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

Customization Options

• To arrange a tailored training for this course, please get in touch with us.

AI-powered test generation encompasses a collection of techniques and tools that automate the creation of test cases and predict testing gaps through machine learning.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals seeking to apply AI techniques to automatically generate tests and identify areas lacking sufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilise AI models to generate effective unit, integration, and end-to-end test scenarios.
• Analyse codebases using machine learning to detect potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimise 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 require this training tailored to your specific toolchain or workflows, please contact us to arrange.

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

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

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

• Generate, prioritise, and maintain tests using AI-driven automation platforms.
• Integrate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilise AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Optimise testing time 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.

Customization Options

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

AI-supported compliance monitoring is a discipline that applies intelligent automation to detect, enforce, and validate policy requirements across the software delivery lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to integrate AI-driven compliance controls into their CI/CD pipelines.

After completing this training, attendees will be equipped to:

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

Format of the Course

• 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 represents a structured methodology for automating model packaging, testing, containerisation, and deployment through continuous integration and continuous delivery pipelines.

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

Upon completing 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 within a controlled environment.

Course Customisation Options

• If your organisation requires tailored pipeline workflows or platform integrations, please contact us to customise this course.

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

This instructor-led live training (available online or on-site) is designed for beginner to intermediate professionals who wish to utilise GitHub Copilot to streamline DevOps tasks, enhance 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.

Course Format

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

Course Customisation Options

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

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

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

By the end of 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.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Prometheus and Grafana are extensively used tools for monitoring modern infrastructure, while machine learning augments these capabilities by providing predictive and intelligent insights to automate operational decisions.

This instructor-led live training (available online or onsite) is designed for observability professionals with intermediate skills who aim to modernize their monitoring infrastructure by incorporating AIOps practices using Prometheus, Grafana, and machine learning techniques.

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

• Configure Prometheus and Grafana to ensure comprehensive observability across systems and services.
• Collect, store, and visualize high-quality time series data.
• Apply machine learning models for anomaly detection and forecasting.
• Develop intelligent alerting rules driven by predictive insights.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical 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.

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

This instructor-led live training, available online or onsite, is tailored for advanced engineers looking to design and implement DevOps automation workflows driven by LLMs and multi-agent systems.

Upon completion 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 agents.
• Orchestrate multiple agents to triage alerts, generate responses, and provide DevOps recommendations.
• Construct secure and maintainable agent-powered workflows using open-source frameworks.

Course Format

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

Course 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) is designed for intermediate-level engineering professionals seeking to enhance their build pipelines through automation, predictive insights, and intelligent caching powered by machine learning.

After completing this course, participants will be able to:

• Utilize ML techniques to evaluate build performance trends.
• Identify and anticipate build failures by analyzing historical build logs.
• Deploy ML-driven caching methods to shorten build times.
• Incorporate predictive analytics into current CI/CD workflows.

Course Format

• Instructor-led lectures combined with interactive discussion.
• 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 tools or environments, please contact us for program customization.