CI/CD for AI: Automating Docker-Based Model Builds and Deployments Training Course

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

This instructor-led, live training (available both online and on-site) is designed for intermediate-level professionals who aim to optimize their deployment pipelines using AI-powered decision-making and resilience features.

Upon completing this training, participants will be able to:

• Implement AI-assisted deployment strategies to ensure safer rollouts.
• Use machine learning insights to predict deployment risks.
• 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 explanations.
• Hands-on scenarios focused 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 is 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 (conducted online or on-site) is designed for intermediate-level DevOps professionals who wish to integrate AI and machine learning into their CI/CD pipelines to improve speed, accuracy, and quality.

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

• Integrate AI tools into CI/CD workflows for intelligent automation.
• Apply AI-based 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.
• 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.

AIOps is a rapidly evolving field that addresses the needs of modern, complex IT environments, especially those operating within cloud architectures. The AIOps Foundation course provides a comprehensive introduction to the concepts, technologies, and practices related to using artificial intelligence in IT operations.

The program delves into the background of AIOps, its core principles, tools, and the organizational challenges faced by IT teams when adopting these approaches.

The training concludes with an exam. Successfully passing it earns you the globally recognized AIOps Foundation certification, which is valid for three years.

Who is it for?

This course is designed for professionals and managers involved 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 increasingly being utilized to predict incidents before they occur and automate root cause analysis (RCA) to minimize downtime and accelerate resolution.

This instructor-led, live training (available both online and on-site) is designed for advanced-level IT professionals who wish 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:

• Build and train machine learning models to identify patterns leading to system failures.
• Automate RCA workflows by correlating data from multiple log sources and metrics.
• 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.
• Numerous exercises and practical 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.

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 online or onsite) is designed for intermediate-level IT operations professionals who want 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:

• 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 automatic incident monitoring and response.

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.

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

This instructor-led, live training (conducted online or on-site) is designed for advanced-level engineers who wish to build and deploy a complete AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom ML models.

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

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

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 test generation is a set of techniques and tools that automate the creation of test cases and predict testing gaps using machine learning.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to apply AI techniques to generate tests automatically and forecast areas of insufficient coverage.

Upon completing this workshop, participants will be prepared to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Analyze codebases with 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 enhanced 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 creating 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 onsite) is designed for intermediate-level QA and DevOps professionals who want 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.
• Use AI for exploratory testing, defect prediction, and test flakiness analysis.
• Optimize testing time and coverage across fast-paced agile projects.

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-supported compliance monitoring is a 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 on-site) is designed for 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.

GitHub Copilot is an AI-driven coding assistant designed to streamline development tasks, including DevOps operations such as writing YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led, live training (available both online and on-site) is tailored for professionals ranging from beginners to intermediates who want to leverage GitHub Copilot to optimize DevOps tasks, enhance automation, and increase productivity.

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

• Utilize GitHub Copilot to aid in shell scripting, configuration management, and CI/CD pipelines.
• Take advantage of AI-driven code completion for YAML files and GitHub Actions.
• Speed up testing, deployment, and automation workflows.
• Use Copilot responsibly by understanding its limitations and adhering to best practices.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical 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.

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

This instructor-led, live training (available online or on-site) is designed for intermediate-level DevOps and security professionals who aim to leverage 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:

• Integrate AI-driven security tools into CI/CD pipelines.
• Utilize static and dynamic analysis powered by AI to detect issues at an earlier stage.
• Automate the detection of secrets, code vulnerability scanning, and dependency risk analysis.
• Implement proactive threat modeling and policy enforcement using intelligent methods.

Format of the Course

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

Course Customization Options

• For 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 in large-scale IT environments.

This instructor-led, live training (available online or on-site) is designed for intermediate-level enterprise IT teams who want 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 cohesive AIOps architecture.
• Correlate metrics, logs, and events across distributed systems using AI-driven analysis.
• Automate incident detection, prioritization, and response with both built-in and custom workflows.
• Optimize performance, reduce Mean Time to Resolution (MTTR), and enhance operational efficiency at an enterprise level.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical 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.

Prometheus and Grafana are widely used tools for observability in modern infrastructure, while machine learning enhances these tools by providing predictive and intelligent insights to automate operational 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 comprehensive observability across systems and services.
• Collect, store, and visualize high-quality time series data effectively.
• Apply machine learning models to detect anomalies and forecast trends.
• Develop intelligent alerting rules based on predictive insights.

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practical 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.

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

This instructor-led, live training (available online or on-site) is designed for advanced-level engineers who aim 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 to achieve smarter automation.
• Automate test generation, commit analysis, and change summaries using these agents.
• Coordinate multiple agents for alert triage, response generation, and DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lectures and discussions.
• Plenty of 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.

Predictive build optimization involves using machine learning to analyze build behavior and enhance reliability, speed, and resource utilization.

This instructor-led, live training (available online or on-site) is designed for intermediate-level engineering professionals who want to improve their build pipelines through automation, prediction, and intelligent caching techniques leveraging machine learning.

Upon completing this course, participants will be able to:

• Use machine learning methods to evaluate build performance patterns.
• Identify and forecast build failures based on historical logs.
• Implement caching strategies driven by machine learning to shorten build times.
• Integrate predictive analytics into existing CI/CD workflows.

Format of the Course

• Lectures guided by an instructor and collaborative discussions.
• Practical exercises centered on analyzing and modeling build data.
• Hands-on implementation within a simulated CI/CD environment.

Course Customization Options

• To tailor this training to specific toolchains or environments, please contact us for customization options.