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 initiate automatic rollbacks when necessary.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals looking to optimize their deployment pipelines with AI-powered decision-making and resilience capabilities.

Upon completing this training, participants will be able to:

• Implement AI-assisted rollout strategies for safer deployments.
• Predict deployment risks 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-driven rollout control leverages machine learning, pattern analysis, and adaptive decision-making models to enhance feature flag operations and canary testing workflows.

This instructor-led training session, available online or onsite, is designed for intermediate-level engineers and technical leads seeking to improve release reliability and optimize feature exposure decisions through AI-powered analysis.

Upon completing this course, participants will be able to:

• Apply AI-based decision models to evaluate the risks associated with new feature exposure.
• Automate canary analysis by utilizing performance, behavioural, and operational indicators.
• Integrate intelligent scoring systems into existing feature flag platforms.
• Design rollout strategies that dynamically adjust based on real-time data.

Course Format

• Guided discussions supported by real-world scenarios.
• Hands-on exercises focused 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 adopted to forecast incidents before they happen and to automate root cause analysis (RCA), thereby reducing downtime and speeding up resolution times.

This live, instructor-led training (available online or on-site) 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 completion of this training, participants will be able to:

• Develop and train machine learning models to identify patterns that lead to 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.

Customization Options for the Course

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

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

This instructor-led training, available both online and onsite, is designed for intermediate IT operations professionals who want to adopt AIOps techniques to correlate metrics and logs, minimize alert noise, and boost 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.
• Alleviate alert fatigue via intelligent filtering and noise suppression.
• Utilize open-source or commercial tools to monitor and respond to incidents automatically.

Course Format

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

Customization Options

• For tailored training on this course, please reach out to us to make arrangements.

Constructing an AIOps pipeline entirely with open-source tools empowers teams to create flexible and cost-efficient solutions for monitoring, detecting anomalies, and managing intelligent alerts in production environments.

This instructor-led live training, available both online and onsite, targets advanced-level engineers eager to build and deploy a complete AIOps pipeline. The course utilizes tools such as Prometheus, ELK, Grafana, and custom machine learning models.

Upon completion of this training, participants will be able 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 and remediation processes using open tools.

Course Format

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

Customization Options

• To arrange customized training for this course, kindly reach out to us.

AI-driven test generation encompasses a suite of techniques and tools that utilize machine learning to automate the creation of test cases and anticipate areas lacking sufficient coverage.

This instructor-led live training, available both online and onsite, is designed for advanced professionals seeking to apply AI methodologies to automatically generate tests and identify zones of inadequate coverage.

Upon completing this workshop, participants will be equipped to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Analyze codebases via machine learning to uncover potential gaps in coverage.
• Integrate AI-based test generation into CI/CD pipelines.
• Refine 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.

Customization Options

• Should you require this training to be tailored to your specific toolchain or workflows, please reach out to us to make arrangements.

AI-driven QA automation upgrades traditional testing methods by creating intelligent test cases, optimizing regression coverage, and embedding smart quality gates into CI/CD pipelines, ensuring 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 want to leverage AI tools to automate and expand quality assurance within continuous integration and deployment workflows.

Upon completing this training, participants will be able to:

• Generate, prioritize, and manage tests using AI-powered automation platforms.
• Embed intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilize AI for exploratory testing, defect prediction, and analysing test flakiness.
• Optimise testing duration and coverage across rapidly evolving agile projects.

Course Format

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

Customization Options

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

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

This live training, led by an instructor (available online or in-person), targets intermediate professionals who want to weave AI-driven compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

• Deploy AI-powered checks to uncover compliance gaps during software builds.
• Utilise intelligent policy engines to uphold regulatory, security, and licensing standards.
• Automatically spot configuration drift and deviations.
• Integrate real-time compliance reporting into delivery workflows.

Course Format

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

Customisation Options

• If your organisation needs tailored compliance integrations, please reach out to 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 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.

By the conclusion of the training, 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 is an AI-driven coding assistant designed to automate development tasks, including DevOps operations like writing YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led live training (available online or onsite) targets beginner to intermediate professionals who want to use 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 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 weaving artificial intelligence into DevOps workflows to proactively identify vulnerabilities, enforce security standards, and automate responses throughout the software delivery process.

This instructor-led, live training (available online or onsite) is designed for intermediate-level DevOps and security professionals looking to leverage AI-driven tools and practices to strengthen security automation within their development and deployment pipelines.

Upon completing this training, participants will be able to:

• Integrate AI-powered security tools into CI/CD pipelines.
• Utilize AI-enabled static and dynamic analysis to identify issues at an earlier stage.
• Automate the detection of secrets, code vulnerability scanning, and dependency risk analysis.
• Implement proactive threat modeling and policy enforcement using intelligent methods.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• 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 within large-scale IT environments.

This instructor-led training, available both online and onsite, 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 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, 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.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Customization Options

• To request a customized version of this course, please contact us to arrange.

Prometheus and Grafana are industry-standard tools for ensuring observability within modern infrastructure, while machine learning elevates these platforms by providing predictive and intelligent insights to automate operational decisions.

This instructor-led live training (available online or on-site) is designed for observability professionals at an intermediate level who aim to modernize their monitoring systems by integrating AIOps methodologies using Prometheus, Grafana, and machine learning techniques.

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

• Configure Prometheus and Grafana to achieve comprehensive observability across various systems and services.
• Collect, store, and visualize high-quality time series data effectively.
• Deploy machine learning models for the purposes of anomaly detection and forecasting.
• Develop intelligent alerting rules derived from predictive insights.

Course Format

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

Course Customization Options

• To request a customized training session for 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 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 intelligent 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.
• Ample 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 behaviors, thereby enhancing reliability, speed, and efficient resource use.

This guided, live training (available online or on-site) targets intermediate engineering professionals seeking to enhance their build pipelines through automation, prediction, and smart caching powered by machine learning.

After completing this course, participants will be able to:

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

Course Format

• Instructor-led sessions combined with collaborative discussions.
• Practical exercises centred on analyzing and modelling build data.
• Hands-on 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 programme.