CI/CD for AI: Automating Docker-Based Model Builds and Deployments Training Course

AI-driven deployment orchestration is a strategy that leverages machine learning and automation to guide the rollout process, identify anomalies, and initiate automatic rollbacks when necessary.

This instructor-led, live training (conducted either online or on-site) is designed for intermediate-level professionals who aim to enhance their deployment pipelines with AI-powered decision-making and resilience features.

Upon completing this training, participants will be able to:

• Implement AI-assisted rollout strategies for safer deployments.
• Predict deployment risks using insights derived from machine learning.
• Integrate automated rollback processes based on anomaly detection.
• Improve observability to support intelligent orchestration.

Format of the Course

• Instructor-led demonstrations with in-depth technical discussions.
• Hands-on scenarios focusing on deployment experimentation.
• Practical labs that simulate real-world orchestration challenges.

Course Customization Options

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

AI for DevOps involves the application of artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization techniques.

This instructor-led, live training (available online or on-site) is designed for intermediate-level DevOps professionals who are looking to integrate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

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

• Integrate AI tools into CI/CD workflows for smarter automation.
• Utilize AI-driven testing, code analysis, and change impact detection.
• Optimize build and deployment strategies using predictive insights.
• Implement traceability and continuous improvement with AI-enhanced feedback loops.

Format of the Course

• Interactive lecture and discussion sessions.
• 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.

AI-driven rollout control is a method that leverages machine learning, pattern analysis, and adaptive decision-making models to manage feature flag operations and canary testing processes.

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers and technical leads who want to enhance release reliability and optimize feature exposure decisions using AI-driven insights.

Upon completing this course, participants will be able to:

• Use AI-based decision models to evaluate the risk associated with new feature releases.
• Automate canary analysis by incorporating performance, behavioral, and operational metrics.
• Integrate intelligent scoring systems into feature flag platforms for better decision-making.
• Develop rollout strategies that adapt dynamically based on real-time data.

Format of the Course

• Guided discussions supported by practical, real-world scenarios.
• Hands-on exercises focusing on AI-enhanced rollout strategies.
• Practical implementation in a simulated feature flag and canary testing environment.

Course Customization Options

• For customized content or to integrate organization-specific tools, please contact us.

AIOps (Artificial Intelligence for IT Operations) is increasingly utilized to predict and prevent incidents before they occur, and to automate root cause analysis (RCA) to minimize downtime and speed up resolution.

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

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

• Develop and train ML models to identify patterns that lead to system failures.
• Automate RCA workflows by correlating data from multiple log and metric sources.
• Integrate alerting and remediation processes into existing platforms.
• Deploy and scale intelligent AIOps pipelines in production environments.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

• For customized training options for this course, please contact us to arrange.

AIOps (Artificial Intelligence for IT Operations) is a practice that leverages machine learning and analytics to automate and enhance IT operations, particularly in monitoring, incident detection, and response.

This instructor-led, live training (available both online and on-site) is designed for intermediate-level IT operations professionals who are looking to implement AIOps techniques. The training will help participants correlate metrics and logs, reduce alert noise, and improve observability through intelligent automation.

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

• Grasp the principles and architecture of AIOps platforms.
• Correlate data from logs, metrics, and traces to pinpoint root causes.
• Mitigate alert fatigue through intelligent filtering and noise reduction.
• Utilize open-source or commercial tools for automated monitoring and incident response.

Format of the Course

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

Course Customization Options

• For a customized training tailored to your specific needs, please contact us to arrange.

An AIOps pipeline constructed entirely with open-source tools enables teams to develop cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting in production environments.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who aim to build and deploy a comprehensive AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Design an AIOps architecture utilizing only open-source components.
• Collect and normalize data from logs, metrics, and traces.
• Apply machine learning models to identify anomalies and predict incidents.
• Automate alerting and remediation processes using open-source tools.

Format of the Course

• Interactive lecture and discussion sessions.
• Extensive 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.

AI-driven test generation is a collection of techniques and tools designed to automate the creation of test cases and predict testing gaps using machine learning.

This instructor-led, live training (available online or on-site) is targeted at advanced-level professionals who aim to apply AI methods for generating tests automatically and identifying areas with 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 using machine learning to identify potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimize test strategies based on predictive failure analytics.

Format of the Course

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

Course Customization Options

• If you need this training tailored to your specific toolchain or workflows, please contact us to arrange.

AI-powered QA automation enhances traditional testing methods by generating intelligent test cases, optimizing regression coverage, and integrating smart quality gates into CI/CD pipelines, ensuring scalable and reliable software delivery.

This instructor-led, live training (available online or on-site) is designed for intermediate-level QA and DevOps professionals who aim to leverage AI tools to automate and scale quality assurance in continuous integration and deployment workflows.

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

• Generate, prioritize, and maintain tests using AI-driven automation platforms.
• Integrate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilize AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Optimize testing time and coverage in fast-paced agile projects.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice 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.

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

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

Upon completion of this program, participants will be empowered to:

• Implement AI-powered assessments to pinpoint compliance gaps during software builds.
• Leverage intelligent policy engines to uphold regulatory, security, and licensing standards.
• Automatically identify configuration drift and deviations.
• Integrate real-time compliance reporting directly into delivery workflows.

Course Format

• Instructor-led presentations reinforced with practical, real-world examples.
• Hands-on exercises centered on authentic CI/CD compliance scenarios.
• Practical experimentation conducted within a secure, controlled DevSecOps lab environment.

Customization Options

• Should your organization require bespoke compliance integrations, please contact us to discuss arrangements.

GitHub Copilot serves as an AI-driven coding companion designed to automate development workflows, covering essential DevOps activities such as authoring YAML configurations, managing GitHub Actions, and creating deployment scripts.

This live, instructor-led training—available either online or on-site—is tailored for professionals at beginner to intermediate levels who aim to leverage GitHub Copilot to streamline DevOps processes, enhance automation capabilities, and significantly boost overall productivity.

Upon completing this program, participants will be equipped to:

• Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipeline development.
• Harness AI-powered code completion specifically within YAML files and GitHub Actions.
• Expedite testing, deployment, and broader automation workflows.
• Apply Copilot responsibly by understanding its AI limitations and adhering to industry best practices.

Course Format

• Engaging lectures combined with interactive discussions.
• Extensive exercises and practical drills.
• Real-time, hands-on implementation within a live-lab environment.

Course Customization Options

• Should you require a customized version of this training, please contact us to make the necessary arrangements.

DevSecOps with AI refers to the strategic integration of artificial intelligence within DevOps pipelines, enabling organisations to proactively identify vulnerabilities, uphold security standards, and automate response measures across the entire software delivery lifecycle.

This instructor-led, live training session, available either online or on-site, is designed for intermediate-level DevOps and security professionals seeking to leverage AI-powered tools and methodologies to strengthen security automation throughout their development and deployment workflows.

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

• Integrate AI-driven security solutions seamlessly into CI/CD pipelines.
• Leverage AI-powered static and dynamic analysis to identify potential issues at earlier stages.
• Automate the detection of secrets, scanning for code vulnerabilities, and analysing dependency risks.
• Implement intelligent techniques for proactive threat modelling and robust policy enforcement.

Course Format

• Engaging lectures followed by interactive discussions.
• Extensive practical exercises to reinforce learning.
• Real-world implementation within a live-lab environment.

Customisation Options

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

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for identifying anomalies, correlating alerts, and automating responses across large-scale 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 existing 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 both built-in and custom workflows.
• Optimise performance, reduce MTTR, and enhance operational efficiency at an enterprise scale.

Course Format

• Interactive lectures and group discussions.
• Extensive practical exercises and hands-on practice.
• Real-time implementation within a live-lab environment.

Course Customisation Options

• To request a customised version of this training, please contact us to make arrangements.

Prometheus and Grafana are widely recognized as essential tools for achieving observability within modern infrastructure, while the integration of machine learning elevates these platforms by delivering predictive and intelligent insights to automate operational decision-making.

This instructor-led, live training—available either online or on-site—is designed for intermediate-level observability professionals seeking to modernise their monitoring infrastructure by incorporating AIOps practices through the use of Prometheus, Grafana, and machine learning techniques.

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

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

Course Format

• Engaging lectures supported by interactive discussions.
• Extensive practical exercises and hands-on practice.
• Real-time implementation within a live-lab environment.

Course Customisation Options

• Should you require a customised version of this training, please contact us to arrange the necessary details.

LLMs and autonomous agent frameworks such as AutoGen and CrewAI are transforming how DevOps teams automate critical tasks—including 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-level engineers seeking to design and implement DevOps automation workflows driven by large language models (LLMs) and multi-agent systems.

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

• Integrate LLM-based agents into CI/CD workflows to enable intelligent automation.
• Leverage agents to automate test generation, commit analysis, and the creation of change summaries.
• Coordinate multiple agents to triage alerts, generate responses, and deliver actionable DevOps recommendations.
• Construct secure, maintainable, agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lectures and collaborative discussions.
• Extensive exercises and hands-on practice.
• Live implementation within a real-time lab environment.

Course Customization Options

• To request a customized version of this training, please contact us to arrange the details.

Predictive build optimization leverages machine learning to examine build behaviour, enhancing reliability, accelerating processes, and optimising resource usage.

This instructor-led, live training, available either online or on-site, is designed for intermediate-level engineering professionals seeking to enhance their build pipelines through automation, predictive analysis, and intelligent caching driven by machine learning.

Upon completing this course, participants will be equipped to:

• Apply machine learning techniques to evaluate patterns in build performance.
• Identify and forecast build failures by analysing historical build logs.
• Deploy machine learning-driven caching strategies to significantly reduce build times.
• Integrate predictive analytics seamlessly into existing CI/CD workflows.

Course Format

• Interactive, instructor-led lectures accompanied by collaborative discussions.
• Practical exercises centred on the analysis and modelling of build data.
• Hands-on implementation within a simulated CI/CD environment.

Course Customisation Options

• To tailor this training to your specific toolchains or operational environments, please contact us to discuss customising the programme.