DevSecOps with AI: Automating Security in the Pipeline 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) is designed for intermediate-level professionals seeking to optimize deployment pipelines through AI-powered decision-making and enhanced resilience.

Upon completing this training, participants will be able to:

• Implement AI-assisted rollout strategies for 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.

Customization Options

• Customized integrations, toolchain support, or workflow alignment can be arranged upon request.

AI for DevOps involves leveraging artificial intelligence to boost continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization strategies.

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

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

• Embedding AI tools into CI/CD workflows to enable intelligent automation.
• Utilizing AI-driven testing, code analysis, and change impact detection.
• Refining build and deployment strategies using predictive analytics.
• Establishing traceability and continuous improvement through AI-enhanced feedback loops.

Course Format

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

Customization Options

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

AI-driven rollout control utilizes machine learning, pattern analysis, and adaptive decision-making models to manage feature flags and canary testing processes.

This instructor-led training, available online or onsite, targets intermediate engineers and technical leads seeking to enhance release reliability and refine feature exposure decisions through AI-powered analysis.

Upon completing this course, participants will be equipped to:

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

Course Format

• Guided discussions underpinned by real-world scenarios.
• Practical exercises focused on AI-enhanced rollout strategies.
• Hands-on implementation within a simulated feature flag and canary testing environment.

Customization Options

• For tailored content or integration with your organization's specific tooling, please get in touch.

AIOps is a rapidly developing discipline designed to meet the demands of contemporary, intricate IT landscapes, particularly those leveraging cloud infrastructure. The AIOps Foundation course provides a thorough overview of the fundamental concepts, technologies, and best practices associated with applying artificial intelligence to IT operations.

The curriculum explores the origins of AIOps, its foundational principles, essential tools, and the organizational hurdles that IT teams encounter when adopting these methodologies.

The training program concludes with an examination. Successfully passing this exam awards the globally recognized AIOps Foundation certification, which remains valid for three years.

Who is this course for?

This program is tailored for professionals and managers engaged in:

IT operations

DevOps and Site Reliability Engineering (SRE)

Cloud architecture

Data analysis and Data Science

Software development

IT security

Product and project management

AIOps (Artificial Intelligence for IT Operations) is gaining traction as a method to anticipate incidents before they happen and automate root cause analysis (RCA), thereby reducing downtime and speeding up resolution times.

This instructor-led 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 equipped to:

• Construct and train ML models to identify patterns indicative of system failures.
• Automate RCA workflows by correlating logs and metrics from multiple sources.
• Integrate alerting and remediation processes into current platforms.
• Deploy and scale intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options

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

AIOps (Artificial Intelligence for IT Operations) is a practice that leverages machine learning and analytics to automate and enhance 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.

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

This instructor-led live training (available online or onsite) targets advanced-level engineers who aim to build and deploy a comprehensive AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom ML models.

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

• Design an AIOps architecture using exclusively open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Implement ML models to identify anomalies and forecast incidents.
• Automate alerting and remediation processes using open tooling.

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.

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

This instructor-led, live training (available online or onsite) targets advanced professionals seeking to apply AI techniques to automatically generate tests and identify areas of insufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Analyze codebases using machine learning to identify potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimize test strategies based on 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

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

This instructor-led live training (available online or on-site) is designed for QA and DevOps professionals with intermediate-level experience who aim 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 maintaining tests through AI-powered automation platforms.
• Embedding intelligent QA gates into CI/CD pipelines to effectively prevent regressions.
• Leveraging AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Enhancing testing efficiency and coverage across rapidly evolving agile projects.

Course Format

• Interactive lectures and group discussions.
• Numerous exercises and practical activities.
• Practical implementation within a live laboratory environment.

Course Customization Options

• For a customized training version of this course, please contact us to make arrangements.

AI-supported compliance monitoring is a discipline that applies intelligent automation to detect, enforce, and validate policy requirements across the software delivery lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to integrate AI-driven compliance controls into their CI/CD pipelines.

After completing this training, attendees will be equipped to:

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

Format of the Course

• 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 either online or onsite, is designed for intermediate professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

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

• Establishing automated pipelines for building and testing AI model containers.
• Implementing version control and reproducibility measures across model lifecycles.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices specifically tailored to machine learning operations.

Course Format

• Instructor-led 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 accordingly.

GitHub Copilot serves as an AI-driven coding assistant designed to automate development activities, encompassing DevOps operations like drafting YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led live training, available either online or onsite, targets beginner to intermediate professionals aiming 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 understanding of AI limitations and best practices.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training (available online or onsite) targets intermediate-level enterprise IT teams aiming to integrate AIOps solutions into their existing observability frameworks and operational processes.

Upon completing this training, participants will be capable of:

• Configuring and integrating Splunk, Moogsoft, and Dynatrace into a cohesive AIOps architecture.
• Correlating metrics, logs, and events across distributed systems through AI-driven analysis.
• Automating incident detection, prioritization, and response using both built-in and custom workflows.
• Optimizing performance, reducing Mean Time to Resolution (MTTR), and enhancing operational efficiency at an enterprise scale.

Course Format

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

Customization Options

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

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 the way 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 (available online or onsite) targets advanced-level engineers who aim to design and implement DevOps automation workflows driven by LLMs and multi-agent systems.

Upon completion of 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.

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.