AI-Driven Deployment Orchestration & Auto-Rollback Training Course

AI for DevOps involves applying artificial intelligence to boost continuous integration, testing, deployment, and delivery processes through intelligent automation and optimisation methods.

This instructor-led live training (available online or onsite) targets intermediate-level DevOps professionals who wish to incorporate AI and machine learning into their CI/CD pipelines to enhance speed, accuracy, and quality.

Upon completion 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.
• Optimise 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.
• Plenty 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 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) represents a methodology 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 either online or on-site—is designed for intermediate-level IT operations professionals seeking to implement AIOps techniques. Participants will learn to correlate metrics and logs, minimize alert noise, and boost observability through intelligent automation.

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

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

Course Format

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

Course Customization Options

• For requests regarding customized training for this course, please contact us to make arrangements.

Implementing an AIOps pipeline composed entirely of open-source tools empowers teams to create cost-efficient and adaptable solutions for monitoring, identifying anomalies, and managing intelligent alerts within production systems.

This instructor-led live training, available online or onsite, is tailored for advanced engineers looking to develop 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 equipped to:

• Architect an AIOps system using exclusively open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Utilise ML models to detect anomalies and forecast incidents.
• Automate alerting and remediation processes using open tooling.

Course Format

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

Customisation Options

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

AI-driven test generation employs techniques and tools that automate the creation of test cases and identify testing gaps using machine learning.

This instructor-led, live training (available online or onsite) targets advanced professionals who want to apply AI techniques to automatically generate tests and forecast areas with insufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilize AI models to create 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.

Course Format

• 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 toolchain or workflows, please contact us to make arrangements.

AI-driven quality assurance automation elevates conventional testing by intelligently generating test cases, refining 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 eager to leverage AI tools to automate and expand 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-powered automation platforms.
• Incorporate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Apply AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Optimise testing time and coverage across fast-paced agile projects.

Course Format

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

Customisation Options

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

AI-driven compliance monitoring is a field that leverages intelligent automation to detect, enforce, and validate policy mandates throughout the software delivery lifecycle.

This instructor-led live training (available online or on-site) targets intermediate-level professionals aiming to embed AI-powered compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be capable of:

• Implementing AI-based checks to pinpoint compliance gaps during software builds.
• Utilising intelligent policy engines to enforce regulatory, security, and licensing standards.
• Automatically identifying configuration drift and deviations.
• Integrating real-time compliance reporting into delivery workflows.

Course Format

• Instructor-guided presentations augmented by practical examples.
• Hands-on exercises centred on real-world CI/CD compliance scenarios.
• Applied experimentation within a controlled DevSecOps lab environment.

Course Customisation Options

• Should your organisation require bespoke compliance integrations, please contact us to arrange.

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

This instructor-led live training, available online or onsite, targets 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 capable of:

• Establishing automated pipelines for the construction and testing of AI model containers.
• Enforcing version control and reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices specifically tailored to machine learning operations.

Course Format

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

Course Customization Options

• Should your organization require 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 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-level professionals keen on utilising GitHub Copilot to streamline DevOps tasks, enhance automation, and boost productivity.

By the conclusion 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.

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 who wish 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:

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

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, linking alerts, and automating responses across extensive IT environments.

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

Upon completing 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 using both built-in and custom workflows.
• Enhance performance, reduce MTTR, and improve operational efficiency at an enterprise scale.

Course Format

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

Course Customization Options

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

Prometheus and Grafana are widely embraced tools for ensuring observability in contemporary infrastructure, while machine learning augments these platforms with predictive and intelligent insights to automate operational decisions.

This instructor-led, live training (available online or on-site) targets intermediate-level observability professionals seeking to modernise their monitoring infrastructure by integrating AIOps practices through the use of Prometheus, Grafana, and ML techniques.

Upon completion of this training, participants will be able 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 anomaly detection and forecasting.
• Develop intelligent alerting rules grounded in predictive insights.

Format of the Course

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

Course Customisation Options

• To request customised 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 tasks like change tracking, test generation, and alert triage by simulating human-like collaboration and decision-making.

This instructor-led live training, available online or onsite, targets advanced-level engineers who wish to design and implement DevOps automation workflows powered 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 smart automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents for triaging alerts, generating responses, and providing DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

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.

Predictive build optimisation involves employing machine learning to examine build behaviours, thereby enhancing reliability, speed, and resource efficiency.

This instructor-led, live training (available online or in-person) is designed for intermediate-level engineering professionals who wish to enhance build pipelines through automation, prediction, and intelligent caching leveraging machine learning techniques.

Upon completing this course, participants will be able to:

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

Course Format

• Instructor-guided lectures and collaborative discussion.
• Practical exercises focused on analysing and modelling build data.
• Hands-on implementation within a simulated CI/CD environment.

Course Customisation Options

• To tailor this training to specific toolchains or environments, please contact us to customise the programme.