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 necessary.

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 involves harnessing artificial intelligence to elevate continuous integration, testing, deployment, and delivery through smart automation and optimisation strategies.

This instructor-led live training, available online or on-site, targets intermediate DevOps professionals keen on embedding AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and overall quality.

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

• Weave AI tools into CI/CD workflows for intelligent automation.
• Deploy AI-driven testing, code analysis, and change impact detection.
• Refine build and deployment strategies by leveraging predictive insights.
• Establish traceability and foster continuous improvement through AI-enhanced feedback loops.

Course Format

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

Course Customisation Options

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

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:

• Leverage AI-based decision models to evaluate the risk associated with exposing new features.
• Automate canary analysis by utilising performance, behavioural, 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 emphasising AI-enhanced rollout strategies.
• Practical implementation in a simulated feature flag and canary environment.

Course Customization Options

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

AIOps (Artificial Intelligence for IT Operations) is increasingly being leveraged 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 online or onsite) is designed for advanced IT professionals who want to implement predictive analytics, automate remediation processes, and design intelligent RCA workflows using AIOps tools and machine learning models.

Upon completing this training, participants will be able to:

• Develop and train machine learning models to detect patterns that precede system failures.
• Automate RCA workflows by correlating 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 practical practice.
• Hands-on implementation in a live lab environment.

Customisation Options

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

AIOps (Artificial Intelligence for IT Operations) is a discipline that leverages machine learning and 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 on-site) targets intermediate IT operations professionals who aim to implement AIOps techniques to correlate metrics and logs, diminish alert noise, and elevate observability through intelligent automation.

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

• Grasping the principles and architecture underpinning AIOps platforms.
• Correlating data across logs, metrics, and traces to pinpoint root causes.
• Mitigating alert fatigue via intelligent filtering and noise suppression.
• Utilizing open-source or commercial tools to monitor and automatically respond to incidents.

Course Format

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

Course Customisation Options

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

Deploying an AIOps pipeline entirely through open-source components empowers teams to architect cost-efficient and adaptable solutions for monitoring, anomaly detection, and intelligent alerting within production environments.

This instructor-led training session, available either online or onsite, is tailored for senior-level engineers seeking to build and deploy a comprehensive AIOps pipeline employing tools such as Prometheus, ELK, Grafana, and bespoke machine learning models.

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

• Architecting an AIOps framework utilising exclusively open-source elements.
• Aggregating and standardising data derived from logs, metrics, and traces.
• Implementing machine learning algorithms to identify anomalies and forecast incidents.
• Automating alert notifications and remediation processes using open-source tooling.

Course Format

• Engaging lectures complemented by interactive discussion.
• Abundant practical exercises and rehearsal opportunities.
• Practical implementation within a live laboratory environment.

Course Customisation Options

• To enquire about a bespoke training programme for this course, please contact us to make arrangements.

AI-driven test generation encompasses the methods and tools that automate the creation of test cases and identify testing gaps through machine learning.

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

Upon completing this workshop, participants will be equipped to:

• Utilise AI models to produce effective unit, integration, and end-to-end test scenarios.
• Analyse codebases using machine learning to uncover potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimise test strategies based on predictive failure analytics.

Course Format

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

Course Customisation Options

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

AI-powered QA automation elevates conventional testing processes by creating intelligent test cases, optimising regression coverage, and embedding smart quality checkpoints into CI/CD pipelines to ensure scalable and dependable software delivery.

This instructor-led live training (available online or onsite) targets intermediate-level QA and DevOps professionals looking to leverage AI tools to automate and scale quality assurance within continuous integration and deployment workflows.

Upon completion of this training, participants will be equipped 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 test flakiness analysis.
• Optimise testing time and coverage across fast-paced agile projects.

Course Format

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

Customisation Options

• To request a tailored training course, please contact us to arrange your requirements.

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

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

Upon 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.

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 organisation requires tailored compliance integrations, please contact us to arrange.

CI/CD for AI represents a structured approach to automating the packaging, testing, containerization, and deployment of models through continuous integration and 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 completion of the training, participants will be capable of:

• Creating automated pipelines for building and testing AI model containers.
• Implementing version control and ensuring reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices tailored specifically 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 conducted 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 is an AI-powered coding assistant that aids in automating development tasks, encompassing DevOps operations such as crafting YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led, live training (available online or onsite) targets beginner to intermediate professionals eager to leverage GitHub Copilot to streamline DevOps tasks, enhance automation, and boost productivity.

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

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

Course Format

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

Course Customization Options

• To request customized training for this course, please get in touch with us to arrange.

Enterprise-grade AIOps platforms such as Splunk, Moogsoft, and Dynatrace deliver robust capabilities for identifying anomalies, correlating alerts, and automating responses across large-scale IT infrastructures.

This instructor-led 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, prioritisation, and response through built-in and custom workflows.
• Optimise performance, reduce MTTR, and enhance operational efficiency at an enterprise scale.

Course Format

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

Course Customisation Options

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

Prometheus and Grafana are extensively adopted tools for ensuring observability within modern infrastructure. By integrating machine learning, these tools are enhanced with predictive and intelligent insights, thereby enabling automated operational decision-making.

This instructor-led live training (available online or onsite) targets intermediate-level observability professionals who aim to modernize their monitoring infrastructure. The course focuses on integrating AIOps practices by leveraging Prometheus, Grafana, and machine learning techniques.

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

• Configure Prometheus and Grafana to establish comprehensive observability across various systems and services.
• Collect, store, and visualise high-quality time series data.
• Deploy machine learning models for effective anomaly detection and forecasting.
• Develop 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.

Customisation Options

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

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 emulating human-like collaboration and decision-making.

This instructor-led live training, available online or onsite, is designed 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.
• Coordinate multiple agents for alert triage, response generation, and DevOps recommendations.
• Develop secure and maintainable agent-powered workflows using open-source frameworks.

Course Format

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

Customisation Options

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

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

This instructor-led, live training (available online or onsite) targets intermediate-level engineering professionals seeking to streamline build pipelines via automation, predictive analytics, and intelligent caching utilising machine learning methods.

After completing this course, participants will be able to:

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

Course Format

• Instructor-guided lectures and collaborative discussions.
• Practical exercises centred on analysing and modelling 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.