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

AI-driven deployment orchestration employs 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) targets intermediate-level professionals seeking to optimize their deployment pipelines with AI-powered decision-making and enhanced resilience capabilities.

Upon completing this training, participants will be able to:

• Deploy AI-assisted rollout strategies for safer release processes.
• Forecast deployment risks using machine learning–driven insights.
• Incorporate automated rollback workflows based on anomaly detection.
• Improve observability to support intelligent orchestration.

Course Format

• Instructor-led demonstrations featuring technical deep dives.
• Hands-on scenarios centered on deployment experimentation.
• Practical labs that simulate 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 optimize continuous integration, testing, deployment, and delivery processes through intelligent automation and enhancement strategies.

This instructor-led live training (available online or on-site) is designed for intermediate-level DevOps professionals looking to embed AI and machine learning into their CI/CD pipelines to boost speed, precision, and overall quality.

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

• Incorporate AI tools into CI/CD workflows for intelligent automation.
• Utilize AI for testing, code analysis, and identifying change impacts.
• Enhance build and deployment strategies through predictive insights.
• Establish traceability and continuous improvement via AI-driven feedback loops.

Course Format

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

Customization Options

• For customized training requests, please contact us to arrange your session.

AI-driven rollout control is an approach that applies machine learning, pattern analysis, and adaptive decision models to feature flag operations and canary testing workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and technical leads who wish to improve release reliability and optimize feature exposure decisions using AI-driven analysis.

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

• Apply AI-based decision models to assess the risk of new feature exposure.
• Automate canary analysis using performance, behavioral, and operational indicators.
• Integrate intelligent scoring systems into feature flag platforms.
• Design rollout strategies that dynamically adjust based on real-time data.

Format of the Course

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

Course Customization Options

• To arrange tailored content or integrate organization-specific tooling, please contact us.

AIOps (Artificial Intelligence for IT Operations) is increasingly being adopted 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 on-site, targets advanced IT professionals looking to implement predictive analytics, automate remediation procedures, and design intelligent RCA workflows using AIOps tools and machine learning models.

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

• Building and training ML models to identify patterns associated with system failures.
• Automating RCA workflows by correlating data from multiple logs and metrics sources.
• Integrating alerting and remediation processes into existing platforms.
• Deploying and scaling intelligent AIOps pipelines within production environments.

Course Format

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

Customization Options for the Course

• For customized training requests, please contact us to arrange.

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

This instructor-led live training (available online or onsite) targets intermediate-level IT operations professionals looking to apply AIOps techniques to correlate metrics and logs, minimize alert noise, and boost observability through intelligent automation.

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

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

Course Format

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

Course Customization Options

• For tailored training requests, please contact us to arrange details.

Developing an AIOps pipeline exclusively with open-source tools enables teams to create cost-efficient and adaptable solutions for observability, anomaly detection, and intelligent alerting within production environments.

This instructor-led live training, available both online and onsite, targets advanced-level engineers looking to design and implement a complete AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

Upon completing this training, participants will be equipped to:

• Architect an AIOps infrastructure relying solely on open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Implement ML models to identify anomalies and forecast incidents.
• Automate alerting and remediation processes using open-source tooling.

Course Format

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

Course Customization Options

• To request tailored training for this course, please get in touch to make arrangements.

AI-driven test generation involves leveraging machine learning to automate the creation of test cases and identify potential gaps in testing coverage.

This instructor-led live training (available online or onsite) is designed for advanced professionals seeking to apply AI techniques for automated test generation and to predict areas lacking sufficient coverage.

After completing this workshop, participants will be able to:

• Utilize AI models to create effective unit, integration, and end-to-end test scenarios.
• Analyze codebases using machine learning to detect potential blind spots in coverage.
• Integrate AI-based test generation into CI/CD workflows.
• Optimize testing strategies based on predictive failure analytics.

Course Format

• Guided technical lectures enriched with expert insights.
• Scenario-based practice sessions and hands-on exercises.
• Practical experimentation within a controlled testing environment.

Customization Options

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

AI-powered QA automation elevates traditional testing by creating smart test cases, optimizing regression coverage, and embedding intelligent quality gates into CI/CD pipelines for scalable, reliable software delivery.

This instructor-led live training (available online or onsite) is designed for intermediate-level QA and DevOps professionals looking to leverage AI tools to automate and scale 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.
• Integrate intelligent QA gates into CI/CD pipelines to prevent regressions.
• Apply AI for exploratory testing, defect prediction, and test flakiness analysis.
• 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

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

AI-assisted compliance monitoring is a specialized field that leverages intelligent automation to identify, 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 who want to embed AI-driven compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

• Implement AI-based checks to pinpoint 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 focused on real-world CI/CD compliance scenarios.
• Applied experimentation within a controlled DevSecOps lab environment.

Course Customization Options

• If your organization needs tailored compliance integrations, please contact us to arrange.

GitHub Copilot serves as an AI-driven coding assistant designed to streamline development workflows, including essential DevOps functions such as creating YAML configurations, setting up GitHub Actions, and scripting deployments.

This instructor-led training session, available both online and onsite, targets beginner to intermediate-level professionals eager to leverage GitHub Copilot for optimizing DevOps processes, enhancing automation capabilities, and increasing overall productivity.

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

• Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipeline development.
• Harness AI-powered code completion within YAML files and GitHub Actions workflows.
• Speed up testing procedures, deployment routines, and automation efforts.
• Deploy Copilot responsibly by gaining insight into AI limitations and adopting industry best practices.

Course Format

• Interactive lectures coupled with group discussions.
• Ample opportunities for exercises and hands-on practice.
• Practical implementation within a live-lab environment.

Course Customization Options

• For personalized training arrangements, please reach out to us.

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 onsite) is designed for intermediate-level DevOps and security professionals who want 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 AI-powered static and dynamic analysis 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.
• Extensive 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 such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for identifying anomalies, correlating alerts, and automating responses across 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 stack and operational workflows.

Upon completing 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 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 make arrangements.

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.

Autonomous agent frameworks, such as AutoGen and CrewAI, in conjunction with Large Language Models (LLMs), are transforming the way DevOps teams automate operations—including change tracking, test creation, and alert triage—by emulating human-like cooperation and decision-making processes.

This instructor-led live training, available either online or onsite, is designed for advanced engineers who want to create and deploy automation workflows in DevOps that are driven by Large Language Models and multi-agent systems.

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

• Incorporating LLM-driven agents into CI/CD pipelines to enable intelligent automation.
• Leveraging agents to automate the generation of tests, analysis of commits, and summarization of changes.
• Orchestrating multiple agents to handle alert triage, formulate responses, and offer DevOps recommendations.
• Constructing secure and easily maintainable workflows powered by agents using open-source frameworks.

Course Format

• Interactive lectures accompanied by discussions.
• Extensive exercises and practical application.
• Practical implementation within a live laboratory environment.

Customization Options for the Course

• To arrange a tailored training session for this course, please reach out to us.

Predictive build optimization involves leveraging machine learning to examine how builds behave, with the goal of enhancing their reliability, speed, and efficient use of resources.

This live training session, led by an instructor and available either online or in person, is designed for engineering professionals at an intermediate level who want to enhance their build pipelines. The focus is on using machine learning methods to achieve automation, make predictions, and implement smart caching techniques.

After finishing this course, participants will be equipped to:

• Utilize machine learning methods to evaluate patterns in build performance.
• Identify and forecast potential build failures by analyzing past build logs.
• Deploy machine learning-driven caching approaches to shorten build times.
• Incorporate predictive analytics into current CI/CD processes.

Course Structure

• Guidance from an instructor accompanied by interactive discussions.
• Practical tasks centered on analyzing and modeling build data.
• Direct implementation experience within a simulated CI/CD setting.

Customization Possibilities

• If you need to tailor this training to specific tools or environments, please reach out to us for a customized program.