Continuous Compliance with AI: Governance in CI/CD Training Course

AI-driven deployment orchestration leverages machine learning and automation to guide rollout strategies, identify anomalies, and initiate automatic rollbacks when necessary.

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

After completing this training, participants will be able to:

• Implement AI-assisted rollout strategies to ensure safer deployments.
• Predict deployment risks using insights driven by machine learning.
• Integrate automated rollback workflows based on anomaly detection.
• Improve observability to support intelligent orchestration.

Course Format

• Instructor-led demonstrations accompanied by technical deep dives.
• Hands-on scenarios focused 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 applying artificial intelligence to improve continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization strategies.

This instructor-led live training (available online or onsite) targets intermediate-level DevOps professionals seeking 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.
• Optimize build and deployment strategies using predictive insights.
• Implement traceability and continuous improvement using AI-enhanced feedback loops.

Course Format

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

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 gaining traction for its ability to anticipate incidents before they happen and automate root cause analysis (RCA), thereby reducing downtime and speeding up resolution times.

This instructor-led live training, available online or onsite, targets advanced IT professionals eager to leverage 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 capable of:

• Developing and training ML models to identify patterns that precede system failures.
• Automating RCA workflows through the correlation of logs and metrics from multiple sources.
• Integrating alerting and remediation mechanisms into existing platforms.
• Deploying and scaling intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options

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

AIOps (Artificial Intelligence for IT Operations) is a discipline 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 online or onsite) targets intermediate-level IT operations professionals aiming to apply AIOps techniques 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.
• 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.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

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

Developing an AIOps pipeline entirely with open-source tools enables teams to create cost-effective and adaptable solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led, live training (available online or onsite) is designed for advanced engineers aiming to build and deploy an end-to-end AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

Upon completing this training, participants will be able to:

• Architect an AIOps system using exclusively open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Apply machine learning models to identify anomalies and predict incidents.
• Automate alerting and remediation processes using open tooling.

Course Format

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

Customization Options

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

AI-driven test generation involves techniques and tools that automate test case creation and identify testing gaps through machine learning.

This instructor-led, live training (available online or onsite) is designed for advanced professionals looking to apply AI methods to automatically generate tests and anticipate areas with insufficient coverage.

After finishing this workshop, participants will be ready to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Examine codebases using machine learning to pinpoint potential coverage blind spots.
• Incorporate AI-based test generation into CI/CD workflows.
• Refine test strategies using predictive failure analytics.

Course Format

• Guided technical lectures enhanced 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 arrange it.

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

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

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

• Create, prioritize, and manage tests using AI-driven 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.
• Optimize testing time and coverage across rapidly evolving agile projects.

Course Format

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

Customization Options

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

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

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

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

• Establishing automated pipelines for constructing and testing 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 adapted for machine learning operations.

Course Format

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

Course Customization Options

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

GitHub Copilot is an AI-driven coding assistant designed to automate development tasks, including DevOps operations such as writing YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level professionals who want to use GitHub Copilot to streamline DevOps tasks, improve 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.

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 seeking to leverage AI-based tools and practices to enhance security automation across development and deployment pipelines.

By the conclusion 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 identify issues at an earlier stage.
• Automate secrets detection, code vulnerability scanning, and dependency risk analysis.
• Implement proactive threat modeling and policy enforcement using intelligent techniques.

Course Format

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

Course Customization Options

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

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace deliver 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 aiming to integrate AIOps tools into their existing observability stack and operational workflows.

Upon completion 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 through built-in and custom workflows.
• Optimize performance, reduce MTTR, and enhance operational efficiency at an enterprise scale.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

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

• Configure Prometheus and Grafana for observability across 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.

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.

Large language models (LLMs) and autonomous agent frameworks such as AutoGen and CrewAI are transforming how DevOps teams automate processes like change tracking, test generation, and alert triage by emulating human-like collaboration and decision-making capabilities.

This instructor-led, live training session (available online or onsite) is designed for advanced engineers looking to design and implement DevOps automation workflows driven by large language models (LLMs) and multi-agent systems.

Upon completing 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 to triage alerts, generate responses, and provide DevOps recommendations.
• Construct secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

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

Course Customization Options

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

Predictive build optimization involves leveraging machine learning to examine build behaviors, thereby enhancing reliability, acceleration, and efficient resource usage.

This guided, live training session (available online or in-person) targets engineering professionals with intermediate skills who aim to enhance their build pipelines by employing automation, predictive capabilities, and smart caching techniques powered by machine learning.

After completing this course, participants will be capable of:

• Utilizing ML methods to evaluate patterns in build performance.
• Identifying and forecasting build failures using historical build logs.
• Deploying ML-driven caching approaches to shorten build times.
• Incorporating predictive analytics into current CI/CD workflows.

Course Format

• Instructor-led lectures accompanied by collaborative discussions.
• Practical activities centered on analyzing and modeling build data.
• Hands-on implementation within a simulated CI/CD environment.

Customization Options for the Course

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