AI for DevOps: Integrating Intelligence into CI/CD Pipelines 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-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 the US (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.

The AI Ops Advanced course expands on foundational AIOps principles to provide practical, hands-on experience with real tools, advanced ML techniques, automation workflows, and operational design patterns. It prepares participants to build, configure, tune, and extend AIOps pipelines and integrations.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced IT professionals who wish to design and implement robust AIOps ecosystems, perform advanced analytics, and automate operational workflows with real-world tools.

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

• Correlate and normalize diverse operational data sources.
• Design and tune anomaly detection and root cause analysis models.
• Integrate AIOps with ITSM and DevOps pipelines.
• Build closed-loop automation and predictive incident workflows.

Format of the Course

• Advanced lectures and architecture discussions.
• Hands-on labs with industry tools and platforms.
• Case studies and optimization exercises.

AIOps (Artificial Intelligence for IT Operations) is the application of machine learning, analytics, and automation to streamline and enhance IT operations. It helps organizations automate incident detection, correlate events, and improve operational decision-making.

This instructor-led, live training (online or onsite) is aimed at beginner-level IT professionals who wish to understand the fundamentals of applying AI and big data in IT operations to improve observability, metrics, and incident handling.

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

• Describe the evolution and importance of AIOps.
• Explain core technologies such as big data and machine learning within AIOps contexts.
• Identify key operational metrics and use cases for AIOps.
• Understand organizational impact, implementation strategies, and challenges.

Format of the Course

• Interactive lecture and discussion.
• Case study review and scenario-based discussion.
• Hands-on exercises with AIOps concepts and tools.

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 normalize 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 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:

• Leverage AI models to generate effective unit, integration, and end-to-end test scenarios.
• Analyze codebases using machine learning to detect 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 supported 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 toolchain or workflows, please contact us to arrange.

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level QA and DevOps professionals who wish to apply 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-moving agile projects.

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 the US (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 like Splunk, Moogsoft, and Dynatrace provide powerful capabilities for detecting anomalies, correlating alerts, and automating responses across large-scale IT environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level enterprise IT teams who wish 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 unified AIOps architecture.
• Correlate metrics, logs, and events across distributed systems using AI-driven analysis.
• Automate incident detection, prioritization, and response with built-in and custom workflows.
• Optimize performance, reduce MTTR, and improve operational efficiency at enterprise scale.

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.

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 the US (online or onsite) is aimed at ML engineers and platform teams who need to build robust operational pipelines for LLM-powered applications at scale.

This instructor-led, live training in the US (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.