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

AI-driven deployment orchestration is an approach that uses machine learning and automation to guide rollout strategies, detect anomalies, and trigger automatic rollback when needed.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to optimize deployment pipelines with AI-powered decision-making and resilience capabilities.

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

• Implement AI-assisted rollout strategies for safer deployments.
• Predict deployment risk using machine learning–driven insights.
• Integrate automated rollback workflows based on anomaly detection.
• Enhance observability to support intelligent orchestration.

Format of the Course

• Instructor-led demonstrations with technical deep dives.
• Hands-on scenarios focused on deployment experimentation.
• Practical labs simulating 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 application of artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization techniques.

This instructor-led, live training (available online or on-site) 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.

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

• Integrate AI tools into CI/CD workflows to enable intelligent automation.
• Apply AI-driven 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.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and hands-on practice.
• Real-time implementation in a live-lab environment.

Course Customization Options

• To request a customized version of this course, please contact us to arrange.

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.

This instructor-led, live training in Uzbekistan (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 is a rapidly evolving field that addresses the needs of modern, complex IT environments—particularly those operating within cloud architectures. The AIOps Foundation course offers a comprehensive introduction to the concepts, technologies, and practices related to the use of artificial intelligence in IT operations.

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

The training concludes with an exam. Passing it grants the globally recognized AIOps Foundation certification, 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 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 practice that applies machine learning and analytics to automate and improve IT operations, particularly in the areas of monitoring, incident detection, and response.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT operations professionals who wish 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 built entirely with open-source tools allows teams to design cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting in production environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to build and deploy an end-to-end AIOps pipeline using tools like Prometheus, ELK, Grafana, and custom ML models.

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

• Design an AIOps architecture using only open-source components.
• Collect and normalise data from logs, metrics, and traces.
• Apply ML models to detect anomalies and predict incidents.
• Automate alerting and remediation using open tooling.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

AI-driven test generation employs techniques and tools that utilize machine learning to automate the creation of test cases and anticipate testing gaps.

This instructor-led live training, available online or onsite, targets advanced professionals seeking to apply AI methods for automatic test generation and identifying areas with insufficient coverage.

After completing this workshop, participants will be equipped to:

• Utilize AI models to produce effective unit, integration, and end-to-end test scenarios.
• Examine codebases using machine learning to pinpoint potential coverage blind spots.
• Incorporate AI-based test generation into CI/CD workflows.
• Refine test strategies through 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.

Course Customization Options

• For training tailored to your specific toolchain or workflows, please contact us to arrange it.

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

This instructor-led, live training (online or on-site) is designed for intermediate-level QA and DevOps professionals who want to apply AI tools to automate and scale quality assurance within continuous integration and deployment workflows.

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

• Generate, prioritise, 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.
• Optimise testing time and coverage across fast-moving agile projects.

Course Format

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

Course Customisation Options

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

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at SRE and DevOps engineers who want to design, build, and safely deploy AI agents for autonomous IT operations.

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer powerful capabilities for detecting anomalies, correlating alerts, and automating responses across large-scale IT environments.

This instructor-led, live training (available online or on-site) is designed for intermediate-level enterprise IT teams aiming to integrate AIOps tools into their existing observability stack and operational workflows.

Upon completion of 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, prioritisation, and response using both built-in and custom workflows.
• Optimise performance, reduce MTTR, and enhance operational efficiency at enterprise scale.

Course Format

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

Course Customisation Options

• To request a customised 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.

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

This instructor-led, live training in Uzbekistan (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.