DevSecOps with AI: Automating Security in the Pipeline Training Course

AI-driven deployment orchestration leverages machine learning and automation to guide rollout strategies, detect anomalies, and trigger automatic rollback when needed.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish 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 refers to leveraging artificial intelligence to optimize continuous integration, testing, deployment, and delivery processes through intelligent automation and enhancement techniques.

This instructor-led live training (available online or onsite) targets intermediate-level DevOps professionals aiming to embed AI and machine learning into their CI/CD pipelines to boost speed, precision, and overall quality.

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

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

Format of the Course

• Interactive lectures and discussions.
• 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 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 becoming a standard approach 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 either online or onsite, targets advanced IT professionals looking to implement 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.
• Embedding alerting and remediation processes into current platforms.
• Deploying and scaling intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options

• For a customized version of this course, please reach out to us to arrange your requirements.

AIOps (Artificial Intelligence for IT Operations) represents a methodology that leverages machine learning and analytics to streamline and enhance IT operations, with a focus on monitoring, incident detection, and response.

This instructor-led live training, available both online and onsite, targets intermediate IT operations professionals seeking to apply AIOps techniques to correlate metrics and logs, minimize alert noise, and enhance observability via intelligent automation.

Upon completing this training, participants will be capable of:

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

Course Format

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

Course Customization Options

• For inquiries regarding customized training for this course, please reach out to us to make arrangements.

An AIOps pipeline developed exclusively with open-source tools empowers teams to create cost-efficient and adaptable solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led, live training session (available online or onsite) is designed for advanced-level engineers seeking to construct 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 capable of:

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

Course Format

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

Course Customization Options

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

AI-driven test generation employs advanced techniques and tools to automate the creation of test scenarios and identify potential gaps in testing coverage through the power of machine learning.

This guided, live training session (available online or at your location) is designed for advanced professionals seeking to apply artificial intelligence techniques to automatically generate tests and anticipate areas where coverage may be lacking.

By the end of this workshop, participants will be equipped to:

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

Course Format

• Technical lectures guided by expert insights.
• Practical exercises and scenario-based learning.
• Hands-on experimentation within a controlled testing environment.

Customization Options

• For training tailored specifically to your tools and workflows, please reach out to us to arrange a session.

AI-powered QA automation elevates conventional testing methodologies by creating 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 online or onsite) targets intermediate-level QA and DevOps professionals seeking to leverage AI tools to automate and scale quality assurance within continuous integration and deployment workflows.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

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

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

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

Upon completing this training, participants will be able to:

• Implement 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 systematic approach to automating the packaging, testing, containerization, and deployment of models through continuous integration and delivery pipelines.

This instructor-led live training, available both online and onsite, is designed for 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 equipped to:

• Develop automated pipelines for constructing and testing AI model containers.
• Establish version control and ensure reproducibility throughout model lifecycles.
• 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 conducted in a controlled environment.

Course Customization Options

• Should your organization require 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, encompassing DevOps operations like crafting YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led live training, available online or onsite, targets beginner to intermediate professionals eager to utilize GitHub Copilot to streamline DevOps tasks, enhance automation, and increase productivity.

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

• Utilizing GitHub Copilot to support shell scripting, configuration, 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, with a clear grasp of AI limitations and best practices.

Course Format

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

Course Customization Options

• For a customized training arrangement, please contact us.

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 seeking to integrate AIOps tools into their existing observability stack 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 extensively used tools for maintaining observability in modern infrastructure, while machine learning augments these platforms with predictive and intelligent insights to automate operational decisions.

This instructor-led, live training (available online or onsite) targets intermediate-level observability professionals seeking to modernize their monitoring infrastructure by integrating AIOps practices through Prometheus, Grafana, and ML techniques.

Upon completing this training, participants will be capable of:

• Configuring Prometheus and Grafana to ensure observability across various systems and services.
• Collecting, storing, and visualizing high-quality time series data.
• Applying machine learning models for anomaly detection and forecasting.
• Developing intelligent alerting rules derived from predictive insights.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

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

Autonomous agent frameworks such as AutoGen and CrewAI, alongside Large Language Models (LLMs), are transforming how DevOps teams automate critical processes like change monitoring, test creation, and alert prioritization by mimicking human-like collaboration and decision-making capabilities.

This instructor-led live training, available online or onsite, is designed for advanced engineers looking to architect and deploy DevOps automation workflows driven by LLMs and multi-agent systems.

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

• Integrate LLM-driven agents into CI/CD pipelines for intelligent automation.
• Automate test generation, commit analysis, and change summaries using agent technologies.
• Orchestrate multiple agents to triage alerts, generate responses, and provide expert DevOps recommendations.
• Construct secure and maintainable agent-powered workflows utilizing open-source frameworks.

Course Format

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

Customization Options

• To arrange a tailored training session for this course, please contact us.

Predictive build optimization involves leveraging machine learning to examine build behaviors, thereby enhancing reliability, execution speed, and resource efficiency.

This instructor-led training session, available either online or on-site, targets intermediate-level engineering professionals looking to enhance their build pipelines through automation, prediction capabilities, and intelligent caching strategies powered by machine learning.

After completing this course, participants will be capable of:

• Utilizing machine learning techniques to evaluate build performance trends.
• Identifying and anticipating build failures by analyzing historical build logs.
• Deploying machine learning-based caching strategies to minimize build times.
• Incorporating predictive analytics into current CI/CD workflows.

Course Format

• Lectures guided by an instructor alongside collaborative discussions.
• Practical exercises centered on the analysis and modeling of 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.