Predictive Build Optimization with Machine Learning Training Course

AI-enabled deployment orchestration leverages machine learning and automation to manage 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 looking to optimize their deployment pipelines using AI-powered decision-making and enhanced resilience.

After completing this training, participants will be able to:

• Implement AI-assisted rollout strategies to ensure safer deployments.
• Forecast deployment risks using machine learning-driven insights.
• Integrate automated rollback workflows triggered by anomaly detection.
• Improve observability to support intelligent orchestration.

Course Format

• Instructor-led demonstrations accompanied by technical deep dives.
• Hands-on scenarios centered around 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 leverages artificial intelligence to enhance continuous integration, testing, deployment, and delivery processes through intelligent automation and optimization techniques.

This instructor-led live training, available either online or onsite, is designed for intermediate-level DevOps professionals who aim to incorporate AI and machine learning into their CI/CD pipelines to boost speed, accuracy, and quality.

Upon completing this training, participants will be capable of:

• Integrating AI tools into CI/CD workflows to enable intelligent automation.
• Applying AI-based testing, code analysis, and change impact detection.
• Optimizing build and deployment strategies using predictive insights.
• Implementing traceability and continuous improvement through AI-enhanced feedback loops.

Course Format

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

Customization Options

• To request customized training for this course, please get in touch with us to arrange it.

AI-driven rollout control is a method that leverages machine learning, pattern analysis, and adaptive decision models to manage feature flag operations and canary testing workflows.

This instructor-led live training (available online or onsite) is designed for intermediate-level engineers and technical leads who want to enhance release reliability and optimize feature exposure decisions through AI-driven analysis.

Upon completing this course, participants will be able to:

• Utilize AI-based decision models to evaluate the risk associated with exposing new features.
• Automate canary analysis by leveraging performance, behavioral, and operational indicators.
• Incorporate intelligent scoring systems into feature flag platforms.
• Develop rollout strategies that dynamically adapt based on real-time data.

Course Format

• Guided discussions enriched with real-world scenarios.
• Hands-on exercises focused on AI-enhanced rollout strategies.
• Practical implementation within a simulated feature flag and canary environment.

Course Customization Options

• For tailored content or integration of organization-specific tooling, please get in touch with us.

AIOps is a swiftly advancing domain designed to meet the demands of contemporary, intricate IT environments, especially those leveraging cloud architectures. The AIOps Foundation course provides a thorough overview of the underlying concepts, technologies, and best practices associated with integrating artificial intelligence into IT operations.

The curriculum delves into the historical context of AIOps, its fundamental principles, essential tools, and the organizational hurdles IT teams often encounter when adopting these methodologies.

Upon completion, participants take an exam. Successfully passing this assessment awards the globally acknowledged AIOps Foundation certification, which remains valid for three years.

Who is this course for?

This programme is tailored for professionals and managers working 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 gaining traction for anticipating incidents before they happen and automating root cause analysis (RCA) to cut down on downtime and speed up resolution times.

This instructor-led live training, available both online and onsite, is tailored for advanced IT professionals looking to implement predictive analytics, automate remediation processes, and design smart RCA workflows using AIOps tools and machine learning models.

Upon completing this training, participants will be equipped to:

• Construct and train machine learning models to identify patterns that lead to system failures.
• Automate RCA workflows by correlating data from multiple sources, including logs and metrics.
• Seamlessly integrate alerting and remediation processes into existing platforms.
• Deploy and scale intelligent AIOps pipelines within production environments.

Course Format

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

Course Customization Options

• For customized training requests for this course, please get in touch with us to make arrangements.

AIOps (Artificial Intelligence for IT Operations) represents a practice that leverages machine learning and analytics to automate and enhance IT operations, specifically in monitoring, incident detection, and response.

This instructor-led live training, available online or onsite, targets intermediate-level IT operations professionals who aim to implement AIOps techniques to correlate metrics and logs, minimize alert noise, and enhance observability through intelligent automation.

Upon completing this training, participants will be able to:

• Grasp the principles and architecture of AIOps platforms.
• Correlate data across logs, metrics, and traces to pinpoint root causes.
• Alleviate alert fatigue via intelligent filtering and noise suppression.
• Utilize open-source or commercial tools to monitor and respond to incidents automatically.

Course Format

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

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

Constructing an AIOps pipeline entirely with open-source tools empowers teams to create cost-efficient and adaptable solutions for monitoring, anomaly identification, and smart alerting within production settings.

This instructor-led, live training (available online or onsite) targets advanced engineers looking to build and implement a complete AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom machine learning models.

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

• Design an AIOps architecture utilising only open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Apply machine learning models to identify anomalies and forecast incidents.
• Automate alerting and remediation processes using open-source tooling.

Course Format

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

Customization Options

• For customized training arrangements for this course, please reach out to us.

AI-driven test generation encompasses a collection of methodologies and tools that leverage machine learning to automate the production of test cases and anticipate testing gaps.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals seeking to apply AI techniques to automatically generate tests and identify areas with insufficient coverage.

Upon completing this workshop, participants will be equipped to:

• Utilize AI models to craft effective unit, integration, and end-to-end test scenarios.
• Analyse codebases using machine learning to uncover potential coverage blind spots.
• Incorporate AI-based test generation into CI/CD workflows.
• Refine test strategies by leveraging 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.

Course Customisation Options

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

AI-driven QA automation elevates conventional testing methods by creating intelligent test cases, optimizing regression coverage, and embedding smart quality gates within CI/CD pipelines to ensure scalable and dependable software delivery.

This instructor-led live training (available online or on-site) targets intermediate QA and DevOps professionals eager to leverage AI tools to automate and scale quality assurance across continuous integration and deployment workflows.

Upon completing this training, participants will be equipped to:

• Create, prioritize, and manage tests using AI-powered automation platforms.
• Embed intelligent QA gates into CI/CD pipelines to mitigate regression risks.
• Utilize AI for exploratory testing, defect prediction, and analyzing test flakiness.
• Streamline testing duration and coverage for rapidly evolving agile projects.

Course Format

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

Customization Options

• For customized training tailored to your needs, please reach out to us for arrangement.

AI-supported compliance monitoring is a discipline that uses intelligent automation to detect, enforce, and validate policy requirements across the software delivery lifecycle.

This instructor-led live training (available online or onsite) is designed for intermediate-level professionals who wish to integrate AI-driven compliance controls into their CI/CD pipelines.

Upon 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.

Course Format

• 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.

CI/CD for AI represents a systematic methodology for automating the packaging, testing, containerization, and deployment of models through continuous integration and continuous delivery pipelines.

This instructor-led live training, available both online and onsite, is designed for intermediate-level professionals aiming to automate end-to-end AI model delivery workflows leveraging Docker and CI/CD platforms.

Upon completion of the training, participants will be equipped to:

• Develop automated pipelines for constructing and validating AI model containers.
• Establish version control and reproducibility mechanisms throughout the model lifecycle.
• Incorporate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically adapted for machine learning operations (MLOps).

Course Format

• Instructor-guided presentations and technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations within a controlled environment.

Course Customization Options

• Should your organization require tailored pipeline workflows or specific platform integrations, please reach out to us to customize this course.

GitHub Copilot acts as an AI-powered coding assistant designed to automate development tasks, including critical DevOps operations such as creating YAML configurations, GitHub Actions, and deployment scripts.

This instructor-led, live training (available online or onsite) is tailored for beginner to intermediate-level professionals who aim to utilize GitHub Copilot to streamline DevOps tasks, enhance automation, and boost overall productivity.

By the conclusion of this training, participants will be equipped to:

• Utilize GitHub Copilot to assist with shell scripting, configuration, and CI/CD pipelines.
• Harness AI code completion features within YAML files and GitHub Actions.
• Accelerate testing, deployment, and automation workflows.
• Apply Copilot responsibly by understanding AI limitations and adhering to best practices.

Course Format

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

Customization Options

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

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

This instructor-led live training (available online or onsite) is designed for intermediate-level DevOps and security professionals looking to leverage AI-driven tools and methodologies to strengthen security automation within their development and deployment pipelines.

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

• Integrating AI-driven security tools into CI/CD pipelines.
• Utilizing AI-powered static and dynamic analysis to identify issues at an earlier stage.
• Automating the detection of secrets, code vulnerability scanning, and dependency risk assessment.
• Implementing proactive threat modeling and policy enforcement through intelligent techniques.

Format of the Course

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

Course Customization Options

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

Enterprise-grade AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer robust functionalities for identifying anomalies, linking alerts, and automating remediation actions within expansive IT infrastructures.

This instructor-led live training (available online or onsite) targets intermediate-level enterprise IT professionals looking to incorporate AIOps solutions into their existing observability stacks and operational processes.

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

• Set up and integrate Splunk, Moogsoft, and Dynatrace into a cohesive AIOps framework.
• Correlate metrics, logs, and events across distributed systems using AI-driven analysis.
• Automate incident detection, prioritization, and response through built-in and custom workflows.
• Enhance performance, decrease MTTR, and boost operational efficiency at an enterprise scale.

Course Delivery Format

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

Customization Options

• To arrange a customized training session for this course, please get in touch with us.

Prometheus and Grafana are industry-standard tools for ensuring observability in contemporary infrastructure. By integrating machine learning, these tools gain the ability to provide predictive and intelligent insights, thereby automating operational decisions.

This instructor-led live training, available both online and onsite, is designed for intermediate-level observability professionals aiming to modernize their monitoring infrastructure. The course focuses on integrating AIOps practices using Prometheus, Grafana, and machine learning techniques.

Upon completing this training, participants will be equipped to:

• Configure Prometheus and Grafana to ensure comprehensive observability across various systems and services.
• Collect, store, and visualize high-quality time-series data effectively.
• Apply machine learning models for effective anomaly detection and forecasting.
• Develop intelligent alerting rules driven by predictive insights.

Course Format

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

Customization Options

• For customized training requests for this course, please reach out to us to make arrangements.