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 rollback procedures when necessary.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals seeking to optimize their 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 to ensure safer deployments.
• Predict deployment risks using insights derived from machine learning.
• Integrate automated rollback workflows triggered by 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 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, accuracy, and quality.

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

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

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practice sessions.
• 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 leverages machine learning, pattern analysis, and adaptive decision models to optimize feature flag operations and canary testing workflows.

This instructor-led live training (available online or onsite) is designed for intermediate-level 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 able to:

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

Course Format

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

Course Customization Options

• For tailored content or integration of organization-specific tools, please contact 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. This approach is particularly effective in the domains of monitoring, incident detection, and response.

This instructor-led training session, available either online or onsite, targets intermediate-level IT operations professionals eager to apply AIOps techniques. The goal is to correlate metrics and logs, minimize alert noise, and enhance observability through intelligent automation.

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

• Grasp the core principles and architectural frameworks of AIOps platforms.
• Correlate data from logs, metrics, and traces to pinpoint root causes effectively.
• Alleviate alert fatigue by employing intelligent filtering and noise suppression strategies.
• Utilize open-source or commercial solutions to automatically monitor and respond to incidents.

Course Format

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

Customization Options

• For tailored training requirements, please reach out to us for arrangement.

Developing an AIOps pipeline entirely with open-source tools enables teams to craft cost-efficient 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-level engineers aiming to build and deploy an end-to-end AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom ML models.

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

• Architecting an AIOps structure using exclusively open-source components.
• Gathering and standardizing data from logs, metrics, and traces.
• Applying machine learning models to identify anomalies and predict incidents.
• Automating alerting and remediation processes using open tooling.

Course Format

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

Customization Options

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

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

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

After completing this workshop, participants will be equipped to:

• Utilise AI models to produce effective unit, integration, and end-to-end test scenarios.
• Analyse codebases using machine learning to detect potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimise test strategies based on predictive failure analytics.

Course Format

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

Course Customisation Options

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

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

This instructor-led, live training (available online or on-site) targets intermediate-level QA and DevOps professionals looking to leverage AI tools to automate and expand 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.
• Embed intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilize AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Optimize testing time and coverage across fast-paced 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 details.

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

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

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

• Implement AI-based checks to identify compliance gaps during software builds.
• Leverage 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 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 offers a structured methodology for automating the packaging, testing, containerization, and deployment of machine learning 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 using Docker and CI/CD platforms.

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

• Establish automated pipelines for building and testing AI model containers.
• Implement version control and ensure reproducibility throughout the model lifecycle.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored to machine learning operations.

Format of the Course

• 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 platform integrations, please contact us to tailor this course accordingly.

GitHub Copilot serves as an AI-driven coding assistant designed to automate development tasks, specifically supporting DevOps operations such as creating YAML configurations, GitHub Actions workflows, and deployment scripts.

This instructor-led live training (available online or onsite) is tailored for beginner to intermediate professionals aiming to utilize GitHub Copilot to streamline DevOps processes, enhance automation capabilities, and increase overall productivity.

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

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

Course Format

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

Course Customization Options

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

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace deliver robust capabilities for identifying anomalies, correlating alerts, and automating responses within 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.

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.
• Enhance performance, reduce MTTR, and improve operational efficiency at an enterprise scale.

Course Format

• Interactive lectures and discussions.
• Extensive 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 make arrangements.

Prometheus and Grafana are extensively adopted tools for ensuring observability in modern infrastructure. The integration of machine learning enriches these platforms with predictive capabilities and intelligent insights, thereby automating operational decision-making.

This instructor-led training, available either online or onsite, is designed for observability professionals with intermediate expertise who aim to modernize their monitoring systems by incorporating AIOps methodologies using Prometheus, Grafana, and machine learning 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 the purpose of anomaly detection and forecasting.
• Constructing intelligent alerting rules grounded in predictive insights.

Format of the Course

• Interactive lectures accompanied by discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• For customized training arrangements for this course, please contact us to discuss your requirements.

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

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

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

• Integrate LLM-based agents into CI/CD workflows to enable smart automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents to triage alerts, generate responses, and provide DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Ample 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.

Predictive build optimization leverages machine learning to analyze build behavior, thereby enhancing reliability, speed, and resource efficiency. <\/p>\n

This instructor-led live training (available online or onsite) is designed for intermediate-level engineering professionals who want to enhance their build pipelines through automation, predictive capabilities, and intelligent caching techniques derived from machine learning. <\/p>\n

After completing this course, participants will be equipped to: <\/p>

• Utilize ML methods to evaluate build performance trends. <\/li>
• Identify and anticipate build failures by analyzing historical build logs. <\/li>
• Deploy ML-driven caching strategies to shorten build times. <\/li>
• Incorporate predictive analytics into current CI\/CD workflows. <\/li> <\/ul>

  Course Format<\/strong> <\/p>

  • Instructor-led lectures alongside collaborative discussions. <\/li>
  • Practical exercises centered on analyzing and modeling build data. <\/li>
  • Hands-on implementation within a simulated CI\/CD environment. <\/li> <\/ul>

    Customization Options<\/strong> <\/p>

    • To tailor this training to specific toolchains or environments, please contact us for program customization. <\/li> <\/ul>