DevSecOps with AI: Automating Security in the Pipeline Training Course

AI-driven deployment orchestration leverages machine learning and automation to manage rollout strategies, identify anomalies, and execute automatic rollbacks when necessary.

This instructor-led live training (available online or on-site) targets intermediate-level professionals aiming to optimize their deployment pipelines with AI-powered decision-making and enhanced resilience.

Upon completing this training, participants will be able to:

• Deploy AI-assisted rollout strategies for safer release processes.
• Predict deployment risks using insights derived from machine learning.
• Integrate automated rollback workflows triggered by anomaly detection.
• Improve observability to support intelligent orchestration.

Course Format

• Instructor-led demonstrations accompanied by technical deep dives.
• Hands-on scenarios centered 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 applying artificial intelligence to optimize and enhance continuous integration, testing, deployment, and delivery processes through intelligent automation.

This instructor-led training session, 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, precision, and overall quality.

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

• Embed AI tools into CI/CD workflows to enable intelligent automation.
• Leverage AI for testing, code analysis, and change impact detection.
• Refine build and deployment strategies by utilizing predictive insights.
• Establish traceability and foster continuous improvement through AI-enhanced feedback loops.

Course Format

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

Customization Options

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

AI-driven rollout control employs machine learning, pattern analysis, and adaptive decision models to optimize feature flag operations and canary testing workflows.

This instructor-led training, available either online or onsite, is designed for intermediate-level engineers and technical leaders seeking to enhance release reliability and refine feature exposure decisions through AI-driven analysis.

Upon completing this course, participants will be capable of:

• Utilizing AI-based decision models to evaluate the risks associated with new feature exposure.
• Automating canary analysis by leveraging performance, behavioral, and operational metrics.
• Incorporating intelligent scoring mechanisms into feature flag platforms.
• Developing rollout strategies that dynamically adapt based on real-time data.

Course Format

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

Customization Options

• For tailored content or integration of organization-specific tooling, please reach out to 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) is a discipline that leverages machine learning and advanced analytics to automate and enhance IT operations, with a specific focus on monitoring, incident detection, and response.

This instructor-led live training (available online or onsite) is designed for intermediate-level IT operations professionals looking 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 flexible solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led live training (available online or onsite) targets advanced engineers seeking to build and deploy an end-to-end 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 utilizing exclusively open-source components.
• Gather 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 tooling.

Course Format

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

Course Customization Options

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

AI-driven test generation encompasses methodologies and tools that utilize machine learning to automate the production of test cases and anticipate areas lacking adequate testing coverage.

This instructor-led, live training (available online or onsite) is designed for advanced professionals seeking to apply AI techniques for automatic test generation and to identify zones with 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.
• Examine codebases using machine learning to uncover potential gaps in coverage.
• Incorporate AI-based test generation into CI/CD workflows.
• Refine test strategies through predictive failure analytics.

Course Format

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

Customization Options

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

AI-enhanced QA automation elevates conventional testing by creating intelligent test cases, improving regression coverage, and embedding smart quality gates within CI/CD pipelines to ensure scalable and dependable software delivery.

This instructor-led live training (available online or onsite) is designed for QA and DevOps professionals at an intermediate level who want 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 managing tests using AI-driven automation platforms.
• Embedding intelligent QA gates into CI/CD pipelines to prevent regressions.
• Utilizing AI for exploratory testing, defect prediction, and test flakiness analysis.
• Optimizing testing duration and coverage across rapidly evolving agile projects.

Course Format

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

Customization Options for the Course

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

AI-driven compliance monitoring is a specialized discipline 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 looking to integrate AI-powered compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

• Apply 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.
• 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 provides a structured framework 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 and CI/CD platforms.

Upon completion of the training, participants will be able 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 (MLOps).

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

• If your organization requires customized pipeline workflows or specific platform integrations, please contact us to tailor this course to your needs.

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

This instructor-led live training, available both online and on-site, targets beginner to intermediate professionals aiming to utilize GitHub Copilot to streamline DevOps processes, enhance automation, and increase overall productivity.

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

• Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipelines.
• Harness AI-driven code completion features within YAML files and GitHub Actions.
• Speed up testing, deployment, and automation workflows.
• Implement 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

• For customized training requests regarding this course, please contact us to arrange details.

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

This instructor-led live training (available online or onsite) is designed for intermediate-level enterprise IT teams seeking to incorporate AIOps solutions into their current observability infrastructure and operational processes.

Upon completing 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-powered analysis.
• Automate incident detection, prioritization, and response through both built-in and custom workflows.
• Enhance performance, decrease MTTR, and boost operational efficiency at an enterprise scale.

Course Format

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

Course Customization Options

• To request a tailored training session 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 processes like change tracking, test generation, and alert triage by emulating human-like collaboration and decision-making.

This instructor-led training (available online or onsite) is designed for advanced engineers who wish 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 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-driven workflows using open-source frameworks.

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.

Predictive build optimization leverages machine learning to analyze how builds perform, with the goal of enhancing reliability, accelerating execution speeds, and improving resource efficiency.

This guided, live training session (available online or at your location) targets engineering professionals with intermediate skills who want to enhance their build pipelines through automation, predictive insights, and smart caching, all powered by machine learning methods.

After completing this course, participants will be capable of:

• Utilizing ML techniques to evaluate patterns in build performance.
• Identifying and anticipating build failures by examining historical build logs.
• Deploying ML-powered caching strategies to shorten build times.
• Incorporating predictive analytics into current CI/CD workflows.

Course Format

• Lectures led by an instructor accompanied by interactive group discussions.
• Practical activities centered on analyzing and modeling build data.
• Practical application within a simulated CI/CD environment.

Customization Options for the Course

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