Continuous Compliance with AI: Governance in CI/CD 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 aiming 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 applies 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 onsite) is designed for intermediate-level DevOps professionals seeking to incorporate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

By the conclusion of this training, participants will be capable of:

• Integrating AI tools into CI/CD workflows for intelligent automation.
• Applying AI-based testing, code analysis, and change impact detection.
• Optimizing build and deployment strategies using predictive insights.
• Implementing traceability and continuous improvement through AI-enhanced feedback loops.

Course Format

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

Customization Options

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

AI-driven release management employs machine learning, pattern recognition, and adaptive decision models to optimize 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 seeking to enhance release reliability and refine feature exposure decisions through AI-based analysis.

Upon completing this course, participants will be capable of:

• Utilizing AI-based decision models to evaluate the risks associated with new feature exposure.
• Automating canary analysis by leveraging performance, behavioral, and operational metrics.
• Integrating intelligent scoring mechanisms into feature flag platforms.
• Developing rollout strategies that dynamically adjust according to real-time data.

Course Format

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

Customization Options

• For tailored content or integration of organization-specific tools, 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 practice that applies machine learning and analytics to automate and improve IT operations, particularly in the areas of monitoring, incident detection, and response.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT operations professionals who wish to implement AIOps techniques to correlate metrics and logs, reduce alert noise, and improve observability through intelligent automation.

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

• Understand the principles and architecture of AIOps platforms.
• Correlate data across logs, metrics, and traces to identify root causes.
• Reduce alert fatigue through intelligent filtering and noise suppression.
• Use open-source or commercial tools to monitor and respond to incidents automatically.

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.

By leveraging exclusively open-source tools, teams can engineer cost-efficient and adaptable solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led live training (available online or onsite) targets advanced engineers aiming to design and deploy a comprehensive AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Architect an AIOps system composed entirely of open-source components.
  
• Gather and standardize data derived from logs, metrics, and traces.
  
• Implement machine learning models to identify anomalies and forecast incidents.
  
• Streamline alerting and remediation processes using open-source tooling.
  

Course Format

• Engaging lectures and interactive discussions.
  
• Extensive hands-on exercises and practice sessions.
  
• Practical implementation within a live-lab environment.
  

Customization Options

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

AI-driven test generation encompasses a suite of techniques and tools that automate the creation of test cases and forecast testing gaps through machine learning.

This instructor-led live training (available online or onsite) is designed for advanced-level professionals seeking to apply AI techniques for automatic test generation and to identify areas with insufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilize AI models to produce effective unit, integration, and end-to-end test scenarios.
• Analyze codebases via machine learning to detect potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Refine test strategies based on predictive failure analytics.

Format of the Course

• Guided technical lectures supplemented 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 specific toolchain or workflows, please contact us to make arrangements.

AI-powered QA automation elevates traditional testing methodologies by generating intelligent test cases, optimizing regression coverage, and embedding smart quality gates within CI/CD pipelines to ensure scalable and dependable software delivery.

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

Upon completing this training, participants will be capable of:

• Creating, prioritizing, and managing tests using AI-driven automation platforms.
• Integrating intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilizing AI for exploratory testing, defect prediction, and test flakiness analysis.
• Optimizing testing time and coverage across fast-moving agile projects.

Course Format

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

Course Customization Options

• To request a customized training for this course, 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 continuous delivery pipelines.

This instructor-led live training, available both online and onsite, targets intermediate-level professionals aiming to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be able to:

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

Course Format

• 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

• For organizations requiring 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 automate development tasks, encompassing DevOps operations such as crafting YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led live training, available either online or on-site, targets beginner to intermediate professionals who aim to utilize GitHub Copilot to streamline DevOps tasks, enhance automation, and increase overall productivity.

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

• Utilizing GitHub Copilot to aid in 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 workflows.
• Applying Copilot responsibly by understanding its limitations and adhering to best practices.

Course Format

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

Customization Options for the Course

• To request customized training for this course, please reach out to us to arrange accordingly.

DevSecOps with AI refers to the integration of 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 want to leverage AI-based tools and practices to enhance security automation across development and deployment pipelines.

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

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

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.

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for detecting anomalies, correlating alerts, and automating responses across large-scale IT environments.

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

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

• Configure and integrate Splunk, Moogsoft, and Dynatrace into a unified AIOps architecture.
• Correlate metrics, logs, and events across distributed systems using AI-driven analysis.
• Automate incident detection, prioritization, and response with built-in and custom workflows.
• Optimize performance, reduce MTTR, and improve operational efficiency at enterprise scale.

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.

Prometheus and Grafana are widely adopted tools for observability in modern infrastructure, while machine learning enhances these tools with predictive and intelligent insights to automate operations decisions.

This instructor-led, live training (online or onsite) is aimed at intermediate-level observability professionals who wish to modernize their monitoring infrastructure by integrating AIOps practices using Prometheus, Grafana, and ML techniques.

Upon completing this training, participants will be able to:

• Configure Prometheus and Grafana to deliver comprehensive observability across various systems and services.
• Collect, store, and visualize high-quality time series data.
• Apply machine learning models for anomaly detection and forecasting.
• Build intelligent alerting rules based on predictive insights.

Course Format

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

Customization Options

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

LLMs and autonomous agent frameworks like AutoGen and CrewAI are redefining how DevOps teams automate tasks such as change tracking, test generation, and alert triage by simulating human-like collaboration and decision-making.

This instructor-led, live training (online or onsite) is aimed at 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 optimization involves leveraging machine learning to examine build behaviors, thereby enhancing reliability, speed, and resource efficiency.

This instructor-led, live training (available online or on-site) is designed for intermediate-level engineering professionals who want to enhance their build pipelines through automation, prediction, and intelligent caching using machine learning techniques.

Upon completing this course, participants will be able to:

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

Course Format

• Instructor-guided lectures and collaborative discussions.
• Practical exercises focused on analyzing and modeling build data.
• Hands-on implementation within a simulated CI/CD environment.

Course Customization Options

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