Self-Healing Pipelines: AI for Automated Incident Detection & Recovery Training Course

AI-driven deployment orchestration is a strategy that leverages machine learning and automation to guide the rollout process, identify anomalies, and initiate automatic rollbacks when necessary.

This instructor-led, live training (conducted either online or on-site) is designed for intermediate-level professionals who aim to enhance their deployment pipelines with AI-powered decision-making and resilience features.

Upon completing this training, participants will be able to:

• Implement AI-assisted rollout strategies for safer deployments.
• Predict deployment risks using insights derived from machine learning.
• Integrate automated rollback processes based on anomaly detection.
• Improve observability to support intelligent orchestration.

Format of the Course

• Instructor-led demonstrations with in-depth technical discussions.
• Hands-on scenarios focusing 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 involves the application of 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 on-site) is designed for intermediate-level DevOps professionals who are looking to integrate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

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

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

Format of the Course

• Interactive lecture and discussion sessions.
• 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 a method that leverages machine learning, pattern analysis, and adaptive decision-making models to manage feature flag operations and canary testing processes.

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers and technical leads who want to enhance release reliability and optimize feature exposure decisions using AI-driven insights.

Upon completing this course, participants will be able to:

• Use AI-based decision models to evaluate the risk associated with new feature releases.
• Automate canary analysis by incorporating performance, behavioral, and operational metrics.
• Integrate intelligent scoring systems into feature flag platforms for better decision-making.
• Develop rollout strategies that adapt dynamically based on real-time data.

Format of the Course

• Guided discussions supported by practical, real-world scenarios.
• Hands-on exercises focusing on AI-enhanced rollout strategies.
• Practical implementation in a simulated feature flag and canary testing environment.

Course Customization Options

• For customized content or to integrate organization-specific tools, please contact us.

AIOps (Artificial Intelligence for IT Operations) is increasingly utilized to predict and prevent incidents before they occur, and to automate root cause analysis (RCA) to minimize downtime and speed up resolution.

This instructor-led, live training (available online or onsite) is designed for advanced-level IT professionals who aim to implement predictive analytics, automate remediation processes, and design intelligent RCA workflows using AIOps tools and machine learning models.

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

• Develop and train ML models to identify patterns that lead to system failures.
• Automate RCA workflows by correlating data from multiple log and metric sources.
• Integrate alerting and remediation processes into existing platforms.
• Deploy and scale intelligent AIOps pipelines in production environments.

Format of the Course

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

Course Customization Options

• For customized training options for this course, please contact us to arrange.

AIOps (Artificial Intelligence for IT Operations) is a practice that leverages machine learning and analytics to automate and enhance IT operations, particularly in monitoring, incident detection, and response.

This instructor-led, live training (available both online and on-site) is designed for intermediate-level IT operations professionals who are looking to implement AIOps techniques. The training will help participants correlate metrics and logs, reduce alert noise, and improve observability through intelligent automation.

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

• Grasp the principles and architecture of AIOps platforms.
• Correlate data from logs, metrics, and traces to pinpoint root causes.
• Mitigate alert fatigue through intelligent filtering and noise reduction.
• Utilize open-source or commercial tools for automated monitoring and incident response.

Format of the Course

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

Course Customization Options

• For a customized training tailored to your specific needs, please contact us to arrange.

An AIOps pipeline constructed entirely with open-source tools enables teams to develop cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting in production environments.

This instructor-led, live training (available online or onsite) is designed for advanced-level engineers who aim to build and deploy a comprehensive AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Design an AIOps architecture utilizing only open-source components.
• Collect and normalize data from logs, metrics, and traces.
• Apply machine learning models to identify anomalies and predict incidents.
• Automate alerting and remediation processes using open-source tools.

Format of the Course

• Interactive lecture and discussion sessions.
• Extensive 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 test generation is a collection of techniques and tools designed to automate the creation of test cases and predict testing gaps using machine learning.

This instructor-led, live training (available online or on-site) is targeted at advanced-level professionals who aim to apply AI methods for generating tests automatically and identifying areas with inadequate 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.

Format of the Course

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

Course Customization Options

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

AI-powered QA automation enhances traditional testing methods by generating intelligent test cases, optimizing regression coverage, and integrating smart quality gates into CI/CD pipelines, ensuring scalable and reliable software delivery.

This instructor-led, live training (available online or on-site) is designed for intermediate-level QA and DevOps professionals who aim to leverage AI tools to automate and scale quality assurance in continuous integration and deployment workflows.

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

• Generate, prioritize, and maintain tests using AI-driven automation platforms.
• Integrate 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 in fast-paced agile projects.

Format of the Course

• Interactive lectures and discussions.
• Extensive 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-enabled compliance monitoring is a specialized field that leverages intelligent automation to detect, enforce, and validate policy mandates throughout the software delivery lifecycle.

This interactive, instructor-led training, available either online or on-site, is designed for intermediate-level professionals seeking to embed AI-driven compliance controls within their CI/CD pipelines.

Upon completion of this program, participants will be empowered to:

• Implement AI-powered assessments to pinpoint compliance gaps during software builds.
• Leverage intelligent policy engines to uphold regulatory, security, and licensing standards.
• Automatically identify configuration drift and deviations.
• Integrate real-time compliance reporting directly into delivery workflows.

Course Format

• Instructor-led presentations reinforced with practical, real-world examples.
• Hands-on exercises centered on authentic CI/CD compliance scenarios.
• Practical experimentation conducted within a secure, controlled DevSecOps lab environment.

Customization Options

• Should your organization require bespoke compliance integrations, please contact us to discuss arrangements.

CI/CD for AI represents a systematic methodology for automating the packaging, testing, containerization, and deployment of models through continuous integration and continuous delivery pipelines.

This live, instructor-led training (available online or on-site) is designed for intermediate-level professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

By the end of the training, participants will be equipped to:

• Develop automated pipelines for building 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 followed by technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic simulations of CI/CD workflows within a controlled environment.

Customization Options

• Should your organization require tailored pipeline workflows or specific platform integrations, please contact us to adapt this course to your needs.

GitHub Copilot serves as an AI-driven coding companion designed to automate development workflows, covering essential DevOps activities such as authoring YAML configurations, managing GitHub Actions, and creating deployment scripts.

This live, instructor-led training—available either online or on-site—is tailored for professionals at beginner to intermediate levels who aim to leverage GitHub Copilot to streamline DevOps processes, enhance automation capabilities, and significantly boost overall productivity.

Upon completing this program, participants will be equipped to:

• Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipeline development.
• Harness AI-powered code completion specifically within YAML files and GitHub Actions.
• Expedite testing, deployment, and broader automation workflows.
• Apply Copilot responsibly by understanding its AI limitations and adhering to industry best practices.

Course Format

• Engaging lectures combined with interactive discussions.
• Extensive exercises and practical drills.
• Real-time, hands-on implementation within a live-lab environment.

Course Customization Options

• Should you require a customized version of this training, please contact us to make the necessary arrangements.

DevSecOps with AI is the practice of integrating artificial intelligence into DevOps pipelines to proactively detect vulnerabilities, enforce security policies, and automate response actions throughout the software delivery lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level DevOps and security professionals who wish to apply AI-based tools and practices to enhance security automation across development and deployment pipelines.

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

• Embed AI-driven security tools into CI/CD pipelines.
• Use static and dynamic analysis powered by AI to detect issues earlier.
• Automate secrets detection, code vulnerability scanning, and dependency risk analysis.
• Enable proactive threat modeling and policy enforcement using intelligent techniques.

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 like Splunk, Moogsoft, and Dynatrace provide powerful capabilities for detecting anomalies, correlating alerts, and automating responses across large-scale IT environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level enterprise IT teams who wish 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 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.

LLMs and autonomous agent frameworks like AutoGen and CrewAI are redefining how DevOps teams automate tasks such as change tracking, test generation, and alert triage by simulating human-like collaboration and decision-making.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to design and implement DevOps automation workflows powered 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 for smart automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents for triaging alerts, generating responses, and providing DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

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.