DevSecOps with AI: Automating Security in the Pipeline Training Course

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

This instructor-led, live training session (available online or onsite) targets intermediate-level professionals seeking to enhance their deployment pipelines with AI-powered decision-making and resilience features.

Upon completing this training, participants will be able to:

• Apply AI-assisted rollout strategies to ensure safer deployments.
• Anticipate deployment risks by leveraging machine learning insights.
• Incorporate automated rollback workflows driven by anomaly detection.
• Improve observability to support intelligent orchestration.

Course Format

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

This instructor-led live training, available online or onsite, is designed for intermediate DevOps professionals aiming to incorporate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

Upon completion, 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 lecture and discussion.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options

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

AI-driven rollout control represents an innovative approach that leverages machine learning, pattern analysis, and adaptive decision models to optimize feature flag operations and canary testing workflows.

This instructor-led live training, available both online and onsite, is designed for intermediate-level engineers and technical leads aiming to enhance release reliability and refine feature exposure decisions through AI-driven analysis.

After completing this course, participants will be able to:

• Utilize AI-based decision models to evaluate the risks associated with introducing new features.
• Automate canary analysis by leveraging performance, behavioral, and operational metrics.
• Incorporate intelligent scoring mechanisms into feature flag platforms.
• Develop rollout strategies that dynamically adapt based on real-time data insights.

Course Format

• Guided discussions enriched with real-world scenarios.
• Practical exercises focused on AI-enhanced rollout strategies.
• Hands-on implementation within a simulated environment for feature flags and canary testing.

Customization Options

• For tailored content or integration with organization-specific tools, please reach out to 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) represents a methodology that leverages machine learning and advanced analytics to automate and enhance IT operations, with a specific focus on monitoring, incident detection, and response processes.

This instructor-led live training, available both online and onsite, is designed for IT operations professionals at an intermediate level who aim to implement AIOps techniques. The goal is to correlate metrics and logs, minimize alert noise, and boost observability through intelligent automation.

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

• Grasping the core principles and architectural frameworks of AIOps platforms.
• Correlating data from logs, metrics, and traces to pinpoint root causes.
• Mitigating alert fatigue via intelligent filtering and noise suppression.
• Utilizing open-source or commercial tools to automate the monitoring and response to incidents.

Course Format

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

Customization Options

• For a tailored training experience for this course, please contact us to make arrangements.

Developing an AIOps pipeline exclusively with open-source tools empowers teams to create affordable 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 who want to build and deploy a comprehensive AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Architecting an AIOps solution using solely open-source components.
• Gathering and standardizing data from logs, metrics, and traces.
• Implementing machine learning models to identify anomalies and predict incidents.
• Automating alerts and remediation processes using open tooling.

Course Format

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

Customization Options

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

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

This instructor-led, live training session (available online or on-site) is designed for advanced professionals seeking to apply AI techniques for automatic test generation and to forecast areas where coverage may be insufficient.

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 using machine learning to uncover potential blind spots in coverage.
• Incorporate AI-based test generation into CI/CD workflows.
• 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

• If you require this training tailored to your specific toolchain or workflows, please contact us to make arrangements.

AI-driven QA automation elevates conventional testing by creating intelligent test cases, refining regression coverage, and embedding smart quality checkpoints into CI/CD pipelines for scalable and dependable software delivery.

This instructor-led, live training (available online or on-site) is designed for 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 capable of:

• Creating, prioritizing, and managing tests using AI-enabled automation platforms.
• Incorporating intelligent QA gates into CI/CD pipelines to prevent regressions.
• Applying AI for exploratory testing, defect prediction, and analysis of test instability.
• Enhancing testing efficiency and coverage across rapidly evolving agile projects.

Course Format

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

Course Customization Options

• To request a tailored training session for this course, please contact us to make arrangements.

AI-assisted compliance monitoring is a discipline that utilizes intelligent automation to detect, enforce, and validate policy requirements throughout the software delivery lifecycle.

This instructor-led, live training (available online or on-site) is designed for intermediate-level professionals who aim to integrate AI-driven compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

• Apply AI-based checks to identify compliance gaps during software builds.
• Utilize intelligent policy engines to enforce regulatory, security, and licensing standards.
• Automatically detect configuration drift and deviations.
• Incorporate real-time compliance reporting into delivery workflows.

Course Format

• Instructor-guided presentations supported by practical examples.
• Hands-on exercises focused on real-world CI/CD compliance scenarios.
• Applied experimentation within a controlled DevSecOps lab environment.

Course Customization Options

• If your organization requires tailored compliance integrations, please contact 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 delivery pipelines.

This instructor-led, live training (available online or onsite) targets intermediate professionals looking to automate end-to-end AI model delivery workflows by leveraging Docker and CI/CD platforms.

Upon completing the training, participants will be equipped to:

• Design automated pipelines for constructing and testing AI model containers.
• Establish version control and reproducibility standards throughout the model lifecycle.
• Incorporate automated deployment strategies for AI services.
• Apply 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 within a controlled environment.

Course Customization Options

• Should your organization require customized pipeline workflows or specific platform integrations, please contact us to tailor this course accordingly.

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

This instructor-led live training, available online or onsite, targets beginner to intermediate professionals aiming to leverage GitHub Copilot to optimize DevOps processes, enhance automation, and increase productivity.

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

• Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipelines.
• Apply AI-driven code completion within YAML files and GitHub Actions.
• Speed up testing, deployment, and automation workflows.
• Implement Copilot responsibly by understanding AI limitations and adhering to best practices.

Format of the Course

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

Course Customization Options

• For customized training requests, 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.

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 activities like change tracking, test generation, and alert triage by mimicking human-like collaboration and decision-making.

This instructor-led live training (available online or onsite) targets 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.
• Develop secure and maintainable agent-powered workflows using open-source frameworks.

Course Format

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

Course Customization Options

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

Upon completing this course, participants will be able to:

• Gain a comprehensive understanding of the architecture and functionality of modern LLMs.
• Create structured and resilient technical prompts for styling, testing, refactoring, and automated QA.
• Develop AI-aware wrappers and microservices integrated into enterprise development ecosystems.
• Execute complete RAG pipelines for organizational knowledge retrieval.
• Oversee security, privacy, and audit considerations for AI-driven code and data interactions.