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.

This instructor-led, live training in Taiwan (online or onsite) is designed for observability and SRE engineers looking to incorporate LLMs and AI into their monitoring, alerting, and incident analysis processes.

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.

This instructor-led, live training offered in Taiwan (either online or on-site) is tailored for SRE and DevOps engineers who aim to design, build, and securely deploy AI agents for autonomous IT operations.

CI/CD for AI represents a systematic methodology designed to automate the packaging, testing, containerization, and deployment of models through continuous integration and continuous delivery pipelines.

This instructor-led live training session, available either online or onsite, targets intermediate-level professionals seeking to streamline end-to-end AI model delivery workflows utilizing Docker alongside CI/CD platforms.

Upon completing the training, participants will gain the ability to:

• Establish automated pipelines for constructing and testing AI model containers.
• Enforce version control and reproducibility standards throughout the model lifecycle.
• Incorporate automated deployment strategies specifically for AI services.
• Apply CI/CD best practices tailored to machine learning operations.

Course Format

• Guided technical discussions and presentations led by the instructor.
• Practical labs featuring hands-on implementation exercises.
• Realistic simulations of CI/CD workflows within a controlled environment.

Customization Options for the Course

• If your organization requires customized pipeline workflows or specific platform integrations, please reach out to us to tailor this course accordingly.

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.

This instructor-led, live training in Taiwan (online or onsite) is designed for ML engineers and platform teams who need to build robust operational pipelines for scalable, LLM-powered applications.

This instructor-led, live training in Taiwan (online or onsite) is aimed at security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered applications.