DevSecOps with AI: Automating Security in the Pipeline Training Course

AI-driven deployment orchestration utilizes machine learning and automation to direct 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 enhance their deployment pipelines with AI-powered decision-making and resilience capabilities.

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 enhance 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 seeking to integrate 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:

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

Course Format

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

Course Customization Options

• For customized training requests, please reach out to us to make arrangements.

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 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 capable of:

• Utilizing AI-based decision models to evaluate the risks associated with exposing new features.
• Automating canary analysis by leveraging performance, behavioral, and operational metrics.
• Integrating intelligent scoring mechanisms into feature flag platforms.
• Designing dynamic rollout strategies that adjust in real-time based on data.

Course Format

• Guided discussions enriched with real-world case studies.
• Practical exercises focused on AI-enhanced rollout strategies.
• Hands-on implementation within a simulated environment for feature flags and canary testing.

Customization Options

• For tailored content or integration with your organization's specific tooling, please reach out to us.

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 live training (available online or onsite) targets advanced IT professionals looking to deploy predictive analytics, automate remediation processes, and design intelligent RCA workflows using AIOps tools and machine learning models.

Upon completing this training, participants will be able to:

• Construct and train machine learning models to identify patterns that precede system failures.
• Automate RCA workflows by correlating logs and metrics from multiple sources.
• Embed alerting and remediation processes into current platforms.
• Deploy and scale intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options

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

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

This instructor-led training, available either online or onsite, is designed for intermediate IT operations professionals looking to apply AIOps techniques. Participants will learn to correlate metrics and logs, minimize alert noise, and boost observability through intelligent automation.

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

• Grasp the core principles and architecture of AIOps platforms.
• Correlate data across logs, metrics, and traces to pinpoint root causes.
• Alleviate alert fatigue via intelligent filtering and noise suppression.
• Utilize open-source or commercial tools to monitor and respond to incidents automatically.

Course Format

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

Course Customization Options

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

An AIOps pipeline developed entirely with open-source tools empowers teams to craft flexible and cost-efficient solutions for monitoring, identifying anomalies, and managing intelligent alerts in production environments.

This instructor-led live training (available online or onsite) targets advanced engineers looking to build 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 able to:

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

Course Format

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

Customization Options

• To request customized training for this course, please get in touch to arrange it.

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

This instructor-led, live training, available both online and onsite, is designed for advanced professionals seeking to apply AI techniques for automatic test generation and the prediction of coverage deficiencies.

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

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

• For training tailored to your specific toolchain or workflows, please contact us to make arrangements.

AI-driven quality assurance (QA) automation elevates conventional testing methods by intelligently generating test cases, refining regression coverage, and embedding smart quality checkpoints into CI/CD pipelines, thereby ensuring scalable and dependable software delivery.

This instructor-led live training, available either online or onsite, is designed for intermediate-level QA and DevOps professionals looking to leverage AI tools to automate and expand quality assurance processes within continuous integration and deployment workflows.

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

• Create, prioritize, and manage tests using AI-powered automation platforms.
• Embed intelligent QA checkpoints into CI/CD pipelines to prevent regression issues.
• 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 group discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

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

AI-assisted compliance monitoring is a discipline that leverages intelligent automation to identify, enforce, and verify policy requirements throughout the software delivery lifecycle.

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

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

• Implement AI-based checks to uncover compliance gaps during software builds.
• Utilize intelligent policy engines to enforce regulatory, security, and licensing standards.
• Automatically detect configuration drift and deviations.
• Embed real-time compliance reporting into delivery workflows.

Course Format

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

Course Customization Options

• For organizations requiring tailored compliance integrations, please contact us to make arrangements.

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

This instructor-led live training, available online or onsite, targets intermediate-level professionals seeking to automate end-to-end AI model delivery workflows utilizing Docker and CI/CD platforms.

Upon completing the training, participants will be equipped to:

• Establish automated pipelines for constructing and testing AI model containers.
• Ensure version control and reproducibility throughout model lifecycles.
• Deploy automated deployment strategies for AI services.
• Apply CI/CD best practices specifically adapted for machine learning operations.

Course Format

• Instructor-led presentations coupled with technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations conducted in a controlled setting.

Course Customization Options

• For organizations requiring tailored pipeline workflows or specific platform integrations, please reach out to customize this course.

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

This instructor-led live training (available online or onsite) targets beginner to intermediate professionals aiming to leverage GitHub Copilot for streamlining DevOps tasks, enhancing automation, and increasing productivity.

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

• Utilize GitHub Copilot to assist with shell scripting, configuration management, and CI/CD pipelines.
• Harness AI code completion capabilities in YAML files and GitHub Actions.
• Speed up testing, deployment, and automation workflows.
• Apply 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 environment.

Course Customization Options

• To request 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 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 current 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.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• 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 industry-standard tools for achieving observability in contemporary infrastructure. By integrating machine learning, these platforms gain the ability to provide predictive and intelligent insights, enabling the automation of operational decisions.

This instructor-led live training, available both online and onsite, is designed for intermediate-level observability professionals aiming to modernize their monitoring infrastructure. The course focuses on integrating AIOps practices through the use of Prometheus, Grafana, and machine learning techniques.

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

• Configuring Prometheus and Grafana to ensure comprehensive observability across various systems and services.
• Collecting, storing, and visualizing high-fidelity time series data.
• Implementing machine learning models for anomaly detection and forecasting.
• Developing intelligent alerting rules driven by predictive insights.

Course Format

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

Customization Options

• To request a customized version of 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 processes like change tracking, test generation, and alert triage by emulating human-like collaboration and decision-making.

This instructor-led live training, available both online and onsite, is designed for advanced engineers aiming to design and implement DevOps automation workflows driven by LLMs and multi-agent systems.

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

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

Course Format

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

Customization Options

• To arrange a customized training version of this course, please contact us directly.

Predictive build optimization involves leveraging machine learning to examine build behaviors, thereby enhancing reliability, accelerating speed, and optimizing resource usage.

This instructor-led live training, available both online and onsite, is designed for engineering professionals with intermediate expertise who aim to upgrade their build pipelines through automation, predictive insights, and smart caching using machine learning methods.

After completing this course, participants will be able to:

• Utilize machine learning techniques to evaluate build performance trends.
• Identify and forecast build failures by analyzing historical build logs.
• Deploy machine learning-driven caching strategies to shorten build times.
• Incorporate predictive analytics into current CI/CD workflows.

Course Format

• Instructor-led lectures accompanied by collaborative discussions.
• Practical exercises centered on analyzing and modeling build data.
• Hands-on implementation within a simulated CI/CD environment.

Customization Options

• To tailor this training to specific toolchains or environments, please reach out to us to customize the program.