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

AI-driven deployment orchestration is a methodology that leverages 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 optimize deployment pipelines through AI-powered decision-making and resilience capabilities.

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

• Deploy AI-assisted rollout strategies to ensure safer deployments.
• Forecast deployment risks using insights derived from machine learning.
• Integrate automated rollback workflows based on anomaly detection.
• Improve observability to support intelligent orchestration.

Course Format

• Instructor-led demonstrations accompanied by technical deep dives.
• Practical scenarios centered on deployment experimentation.
• Hands-on 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 refers to the strategic application of artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization.

This instructor-led live training (available online or onsite) is designed for intermediate-level DevOps professionals seeking to incorporate AI and machine learning into their CI/CD pipelines to improve speed, accuracy, and overall quality.

Upon completion of this training, participants will be equipped 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 using AI-enhanced feedback loops.

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.

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

This instructor-led training, available online or on-site, is designed for intermediate-level engineers and technical leads seeking to enhance release reliability and refine feature exposure decisions through AI-driven analytics.

Upon completing this course, participants will be equipped to:

• Implement AI-based decision models to evaluate the risk associated with new feature exposure.
• Automate canary analysis by leveraging performance, behavioral, and operational metrics.
• Integrate intelligent scoring mechanisms into feature flag platforms.
• Develop dynamic rollout strategies that adjust in real-time based on live data.

Course Format

• Guided discussions supported by real-world case studies.
• Hands-on exercises focused on AI-enhanced rollout strategies.
• Practical implementation within a simulated feature flag and canary testing environment.

Customization Options

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

This instructor-led, live training in Turkey (online or onsite) is aimed at observability and SRE engineers who want to integrate LLMs and AI into their monitoring, alerting, and incident analysis workflows.

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) involves leveraging machine learning and analytics to automate and enhance IT operations, with a focus 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.

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 identify root causes.
• Decrease alert fatigue by employing intelligent filtering and noise suppression.
• Utilize open-source or commercial tools to monitor and respond to incidents automatically.

Format of the Course

• Interactive lecture and discussion.
• Plenty 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.

Utilizing a fully open-source stack for an AIOps pipeline enables teams to develop cost-efficient and adaptable solutions for monitoring, identifying anomalies, and managing intelligent alerts within production environments.

This instructor-led live training (available online or in-person) targets advanced engineers looking to build and deploy a comprehensive AIOps pipeline employing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Design an AIOps architecture utilizing exclusively open-source components.
• Gather and standardize data originating from logs, metrics, and traces.
• Implement ML models to identify anomalies and forecast incidents.
• Automate alerting and remediation processes using open tooling.

Course Format

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

Customization Options

• For those wishing to request a tailored training session for this course, please reach out to us to arrange details.

AI-driven test generation encompasses a collection of techniques and tools that utilize machine learning to automate the creation of test cases and predict gaps in testing coverage.

This instructor-led, live training (available online or onsite) is designed for advanced professionals seeking to apply AI methodologies to automatically generate tests and identify areas 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.
• Analyze codebases using machine learning to uncover potential coverage blind spots.
• Integrate AI-based test generation into CI/CD workflows.
• Optimize 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

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

AI-driven QA automation elevates traditional testing by creating intelligent test cases, enhancing regression coverage, and embedding smart quality gates into CI/CD pipelines, ensuring scalable and dependable software delivery.

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

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

• Create, prioritize, and manage tests using AI-powered automation platforms.
• Implement intelligent QA gates within CI/CD pipelines to prevent regressions.
• Apply AI for exploratory testing, defect prediction, and analysis of test flakiness.
• Enhance testing efficiency and coverage across fast-paced agile projects.

Course Format

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

Customization Options for the Course

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

This live, instructor-led training in Turkey (available online or onsite) is designed for SRE and DevOps engineers seeking to design, build, and safely deploy AI agents for autonomous IT operations.

AI-driven compliance monitoring is a field that utilizes intelligent automation to detect, enforce, and verify policy requirements throughout the software delivery lifecycle.

This instructor-led, live training (available online or on-site) targets intermediate-level professionals aiming to incorporate AI-powered compliance controls into their CI/CD pipelines.

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

• If your organization requires customized compliance integrations, please contact us to arrange.

Enterprise-grade 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, targets intermediate-level enterprise IT teams looking to integrate AIOps tools into their current observability stacks and operational workflows.

Upon completing this training, participants will be capable of:

• Configuring and integrating Splunk, Moogsoft, and Dynatrace into a cohesive AIOps architecture.
• Correlating metrics, logs, and events across distributed systems using AI-driven analysis.
• Automating incident detection, prioritization, and response through both built-in and custom workflows.
• Enhancing performance, reducing MTTR, and boosting operational efficiency at an enterprise scale.

Course Format

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

Course Customization Options

• For customized training requests regarding 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.

This instructor-led live training in Turkey (online or onsite) targets ML engineers and platform teams who need to build robust operational pipelines for large-scale LLM-powered applications.

This instructor-led, live training in Turkey (online or onsite) is aimed at security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered applications.