AI for DevOps: Integrating Intelligence into CI/CD Pipelines 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 (available online or onsite) is designed for intermediate-level professionals seeking to optimise 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

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

AI-driven rollout control is a methodology that utilises machine learning, pattern analysis, and adaptive decision models to optimise feature flag operations and canary testing workflows.

This instructor-led live training, available both online and onsite, is designed for intermediate-level engineers and technical leads seeking to enhance release reliability and refine feature exposure decisions through AI-driven analysis.

Upon completing this course, participants will be able to:

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

Course Format

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

Course Customisation Options

• To arrange tailored content or integrate organisation-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) is a discipline that leverages machine learning and analytics to automate and enhance IT operations, with a specific focus on monitoring, incident detection, and response.

This instructor-led live training, available both online and onsite, targets intermediate-level IT operations professionals seeking to implement AIOps techniques. The course aims to help participants correlate metrics and logs, reduce alert noise, and improve 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 pinpoint root causes.
• Mitigate alert fatigue via intelligent filtering and noise suppression.
• Employ open-source or commercial tools to monitor incidents and trigger automated responses.

Course Format

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

Course Customization Options

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

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

This instructor-led training, available both online and onsite, is designed for advanced engineers aiming to implement an end-to-end AIOps pipeline. Key tools covered include Prometheus, ELK, Grafana, and custom machine learning models.

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

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

Course Format

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

Customization Options

• For customized training arrangements, please contact us directly.

AI-driven test generation employs automated techniques and machine learning tools to create test cases and identify potential testing gaps.

This instructor-led live training, available either online or onsite, is designed for advanced professionals looking to implement AI methods for automatic test generation and the prediction of insufficient coverage areas.

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 through machine learning to uncover potential coverage blind spots.
• Incorporate AI-based test generation into CI/CD workflows.
• Optimise 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.

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 methods by creating intelligent test cases, enhancing regression coverage, and embedding smart quality checkpoints into CI/CD pipelines, ensuring scalable and dependable software delivery.

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

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

• Create, prioritise, and upkeep tests using AI-driven automation platforms.
• Incorporate intelligent QA checkpoints into CI/CD pipelines to prevent regressions.
• Apply AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Enhance testing efficiency and coverage across rapid agile project cycles.

Course Format

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

Customisation Options

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

AI-assisted compliance monitoring is a field that utilises smart automation to identify, enforce, and verify policy requirements throughout the software delivery lifecycle.

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

Upon completing this training, participants will be capable of:

• Implementing AI-based checks to uncover compliance gaps during software builds.
• Leveraging intelligent policy engines to uphold regulatory, security, and licensing standards.
• Automatically detecting configuration drift and deviations.
• Embedding real-time compliance reporting into delivery workflows.

Course Format

• Instructor-guided presentations backed 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 systematic methodology for automating the packaging, testing, containerization, and deployment of machine learning models through continuous integration and delivery pipelines.

This instructor-led live training, available in online or onsite formats, targets intermediate-level professionals seeking to automate end-to-end AI model delivery workflows leveraging Docker and CI/CD platforms.

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

• Develop automated pipelines for constructing and testing AI model containers.
• Establish version control and ensure reproducibility throughout model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored to machine learning operations.

Course Format

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

Course Customization Options

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

GitHub Copilot serves as an AI-driven coding assistant designed to streamline development activities, particularly DevOps operations like crafting YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led live training, available both online and onsite, is tailored for beginners to intermediate professionals aiming to utilise GitHub Copilot to optimise DevOps tasks, enhance automation, and increase overall productivity.

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

• Leveraging GitHub Copilot to support shell scripting, configuration management, and CI/CD pipelines.
• Harnessing AI code completion features within YAML files and GitHub Actions.
• Speeding up testing, deployment, and automation processes.
• Applying Copilot responsibly, with a clear grasp of AI limitations and best practices.

Course Format

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

Course Customisation Options

• For bespoke training arrangements, please get in touch with us.

DevSecOps with AI involves integrating artificial intelligence into DevOps pipelines to proactively identify vulnerabilities, enforce security policies, and automate response actions throughout the software delivery lifecycle.

This instructor-led, live training (available online or onsite) is designed for intermediate-level DevOps and security professionals seeking to apply AI-based tools and practices to enhance security automation across development and deployment pipelines.

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

• Integrate AI-driven security tools into CI/CD pipelines.
• Leverage AI-powered static and dynamic analysis to detect issues earlier.
• Automate secrets detection, code vulnerability scanning, and dependency risk analysis.
• Enable proactive threat modeling and policy enforcement using intelligent techniques.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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-grade AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for identifying anomalies, correlating alerts, and automating responses across expansive IT environments.

This instructor-led training, available online or onsite, is designed for intermediate-level enterprise IT teams looking to incorporate AIOps tools into their current observability frameworks and operational workflows.

Upon completing this training, participants will be equipped 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.
• Enhance performance, reduce MTTR, and boost operational efficiency at an enterprise scale.

Course Format

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

Customisation Options

• To request a tailored training session for this course, please get in touch to make arrangements.

Prometheus and Grafana are industry-standard tools for ensuring observability within modern infrastructure. By integrating machine learning, these platforms gain the ability to deliver predictive and intelligent insights, thereby automating operational decision-making.

This instructor-led live training, available either online or onsite, is designed for observability professionals with intermediate-level expertise. It aims to help participants modernise their monitoring infrastructure by incorporating AIOps practices using Prometheus, Grafana, and machine learning techniques.

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

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

Course Format

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

Course Customisation Options

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

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 mimicking human-like collaboration and decision-making.

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

By the end 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 to triage alerts, generate responses, and provide DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

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

This instructor-led, live training (available online or onsite) targets engineering professionals with intermediate skills who aim to enhance build pipelines through automation, prediction, and smart caching utilizing machine learning methodologies.

Upon completing this course, participants will be able to:

• Utilize machine learning techniques to evaluate build performance patterns.
• Identify and forecast build failures using historical build logs.
• Deploy machine learning-driven caching strategies to shorten build durations.
• Incorporate predictive analytics into current CI/CD workflows.

Course Format

• Lectures guided by the instructor coupled with collaborative discussions.
• Practical exercises centred on analysing and modelling build data.
• Practical implementation within a simulated CI/CD environment.

Course Customization Options

• To tailor this training to specific toolchains or environments, please get in touch with us to customize the program.