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 necessary.

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

Upon completing this training, participants will be able to:

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

Course Format

• 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 involves applying 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) targets intermediate-level DevOps professionals looking to incorporate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and overall quality.

By the conclusion of this training, participants will be equipped 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.

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.

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 increasingly being utilized to anticipate incidents prior to their occurrence and automate root cause analysis (RCA), thereby reducing downtime and expediting resolution times.

This instructor-led, live training (available online or onsite) is designed for advanced IT professionals seeking 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:

• Constructing and training machine learning models to identify patterns that lead to system failures.
• Automating RCA workflows through the correlation of multi-source logs and metrics.
• Integrating alerting and remediation processes into existing platforms.
• Deploying and scaling intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options for the Course

• To request 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.

Organizations can create cost-efficient and adaptable solutions for monitoring, anomaly detection, and intelligent alerting in production environments by building an AIOps pipeline entirely with open-source tools.

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

Upon completing this training, participants will be able to:

• Design an AIOps architecture relying solely on open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Apply ML models to identify anomalies and anticipate incidents.
• Automate alerting and remediation processes using open tooling.

Course Format

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

Course Customization Options

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

AI-powered test generation encompasses a collection of methods and tools that automate the creation of test cases and identify testing gaps through machine learning.

This guided, live training session (available online or in-person) is designed for advanced professionals looking to apply AI techniques to automatically generate tests and predict areas where coverage may be lacking.

After completing this workshop, participants will be able to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Analyze 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

• Technical lectures guided by expert insights.
• Scenario-based practice sessions and hands-on exercises.
• Applied experimentation within a controlled testing environment.

Customization Options

• If you require this training to be tailored to your specific toolchain or workflows, please contact us to arrange it.

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

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

• Generate, prioritize, and maintain tests using AI-driven automation platforms.
• Incorporate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilize AI for exploratory testing, defect prediction, and analyzing test flakiness.
• Optimize testing time and coverage across rapidly evolving 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 session for this course, please contact us to arrange.

AI-driven 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 seeking to integrate AI-powered 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.
• Integrate real-time compliance reporting into delivery workflows.

Course Format

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

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 AI models through continuous integration and delivery pipelines.

This instructor-led live training, available online or onsite, is designed for intermediate-level professionals seeking to automate end-to-end AI model delivery workflows using Docker alongside 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 the model lifecycle.
• Incorporate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically adapted for machine learning operations.

Course Format

• Instructor-led presentations accompanied by 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 reach out to 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) is designed for 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.

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

This instructor-led live training, available online or onsite, targets intermediate-level enterprise IT teams seeking to incorporate AIOps tools into their existing observability stacks and operational workflows.

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

• Configure and integrate Splunk, Moogsoft, and Dynatrace into a cohesive 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 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 tasks like change tracking, test generation, and alert triage by mimicking human-like collaboration and decision-making.

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

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

• Integrating LLM-based agents into CI/CD workflows for intelligent automation.
• Automating test generation, commit analysis, and change summaries through agents.
• Coordinating multiple agents to triage alerts, generate responses, and provide DevOps recommendations.
• Building secure and maintainable agent-powered workflows using open-source frameworks.

Course Format

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

Course Customization Options

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

Predictive build optimization involves leveraging machine learning to analyze build behavior, enhancing reliability, speed, and resource utilization.

This instructor-led, live training (available online or onsite) targets intermediate-level engineering professionals looking to enhance build pipelines through automation, prediction, and intelligent caching using machine learning techniques.

Upon completion of this course, attendees will be able to:

• Apply ML techniques to evaluate build performance patterns.
• Detect and forecast build failures using historical build logs.
• Implement ML-driven caching strategies to reduce build durations.
• Integrate predictive analytics into existing CI/CD workflows.

Format of the Course

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

Course Customization Options

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