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

Intelligent deployment orchestration leverages machine learning and automation to steer release strategies, identify irregularities, and initiate automatic rollback procedures when necessary.

Designed for intermediate-level professionals seeking to refine deployment pipelines through AI-powered decision-making and resilience, this instructor-led live training can be delivered online or on-site.

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

• Deploy AI-assisted strategies to ensure safer releases.
• Forecast deployment risks using insights derived from machine learning.
• Embed automated rollback workflows triggered by anomaly detection.
• Strengthen observability to facilitate intelligent orchestration.

Course Delivery Model

• Instructor-led demonstrations featuring technical deep dives.
• Hands-on scenarios dedicated to deployment experimentation.
• Practical labs that mimic real-world orchestration challenges.

Tailored Course Options

• Custom integrations, toolchain support, or workflow alignment can be arranged upon request.

AI in DevOps leverages artificial intelligence to elevate continuous integration, testing, deployment, and delivery through intelligent automation and advanced optimization strategies.

This instructor-led live training, available either online or on-site, is designed for intermediate-level DevOps professionals seeking to embed AI and machine learning capabilities into their CI/CD pipelines to enhance speed, precision, and quality.

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

• Incorporate AI tools into CI/CD workflows to achieve intelligent automation.
• Utilize AI-driven testing, code analysis, and change impact detection methods.
• Refine build and deployment strategies by leveraging predictive insights.
• Establish traceability and continuous improvement mechanisms through AI-enhanced feedback loops.

Course Format

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

Customization Options

• For tailored training requirements regarding this course, please reach out to us to discuss arrangements.

AI-driven rollout management leverages machine learning, pattern recognition, and adaptive decision-making models to enhance feature flag operations and canary testing workflows.

Delivered as a live, instructor-led session (either online or on-site), this course is tailored for intermediate-level engineers and technical leads aiming to boost release reliability and refine feature exposure decisions through AI-powered analysis.

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

• Utilize AI-based decision models to evaluate the risks associated with exposing new features.
• Automate canary analysis by incorporating performance, behavioral, and operational metrics.
• Embed intelligent scoring systems within existing feature flag platforms.
• Develop rollout strategies that dynamically adapt in response to real-time data.

Course Delivery Method

• Facilitated discussions grounded in practical, real-world scenarios.
• Practical exercises focused on AI-enhanced rollout strategies.
• Implementation tasks within a simulated feature flag and canary environment.

Customization Availability

• Please reach out if you require tailored content or integration with organization-specific tools.

This live, instructor-led training in Denmark (online or onsite) is tailored for observability and SRE engineers looking to embed LLMs and AI into their monitoring, alerting, and incident analysis operations.

The application of AIOps (Artificial Intelligence for IT Operations) is becoming increasingly prevalent for forecasting system incidents prior to occurrence and streamlining root cause analysis (RCA), thereby reducing downtime and expediting resolution times.

This instructor-led, live training session (available online or on-site) is designed for advanced IT professionals seeking to implement predictive analytics, automate remediation processes, and construct intelligent RCA workflows leveraging AIOps tools and machine learning models.

Upon completion of this course, participants will have the ability to:

• Develop and train ML models capable of identifying patterns that precede system failures.
• Streamline RCA workflows through the correlation of logs and metrics from multiple sources.
• Embed alerting and remediation mechanisms into current operational platforms.
• Implement and scale intelligent AIOps pipelines within production environments.

Course Structure

• Engaging lectures and interactive discussions.
• Extensive exercises and practical application.
• Live implementation within a lab environment.

Customization Options

• For customized training tailored to this curriculum, please reach out to us to make arrangements.

AIOps (Artificial Intelligence for IT Operations) leverages machine learning and advanced analytics to streamline and enhance IT operations, with a specific focus on optimizing monitoring, incident detection, and response workflows.

Delivered as an instructor-led live training session (either online or onsite), this course is designed for intermediate-level IT operations professionals seeking to apply AIOps techniques. The curriculum focuses on correlating metrics and logs, mitigating alert noise, and enhancing observability through intelligent automation.

Upon completion, participants will be equipped to:

• Grasp the core principles and architectural components of AIOps platforms.
• Correlate data across logs, metrics, and traces to accurately identify root causes.
• Mitigate alert fatigue by implementing intelligent filtering and noise suppression strategies.
• Deploy open-source or commercial tools to automate monitoring and incident response.

Course Delivery Format

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

Customization Availability

• For a tailored training experience, please reach out to us to discuss your specific requirements.

By leveraging an AIOps pipeline composed entirely of open-source tools, teams can develop cost-efficient and adaptable solutions for monitoring, anomaly detection, and smart alerting within production environments.

This instructor-led live training, available online or onsite, is tailored for senior engineers seeking to construct and deploy a comprehensive AIOps pipeline utilizing tools such as Prometheus, ELK, Grafana, and custom machine learning models.

Upon completion, participants will be equipped to:

• Architect an AIOps system relying exclusively on open-source components.
• Gather and standardize data from logs, metrics, and traces.
• Utilize ML models to identify anomalies and forecast potential incidents.
• Streamline alerting and remediation processes through open-source tooling.

Course Format

• Interactive lectures paired with facilitated discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory setting.

Customization Options

• For tailored training arrangements, please reach out to us to discuss your requirements.

AI-driven test generation represents a suite of methodologies and tools that utilise machine learning to automate the creation of test cases and predict potential gaps in testing coverage.

This live, instructor-led training, available both online and onsite, is designed for advanced-level professionals seeking to leverage AI techniques to automatically generate tests and forecast areas where coverage is insufficient.

By the end of this workshop, participants will be equipped to:

• Utilise AI models to create effective unit, integration, and end-to-end test scenarios.
• Examine codebases using machine learning to identify potential blind spots in coverage.
• Incorporate AI-based test generation into CI/CD workflows.
• Refine test strategies using predictive failure analytics.

Course Structure

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

Customisation Options

• Should you require this training to be adapted to your specific toolchain or workflows, please get in touch to make arrangements.

Leveraging AI in QA automation elevates conventional testing practices by generating intelligent test cases, refining regression coverage, and embedding smart quality gates within CI/CD pipelines. This approach ensures software delivery that is both scalable and highly reliable.

Designed for intermediate-level QA and DevOps specialists, this live, instructor-led training—available either online or onsite—focuses on applying AI tools to automate and expand quality assurance efforts within continuous integration and deployment environments.

Upon completion, participants will be equipped to:

• Create, rank, and sustain test suites using AI-driven automation platforms.
• Embed intelligent QA checkpoints into CI/CD pipelines to effectively prevent regressions.
• Leverage AI for exploratory testing, defect forecasting, and analyzing test flakiness.
• Enhance testing efficiency and coverage in fast-paced agile project settings.

Training Structure

• Engaging lectures and collaborative discussions.
• Extensive practical exercises and skill-building tasks.
• Real-world implementation within a live laboratory environment.

Customization Availability

• For tailored training tailored to this curriculum, please reach out to us to discuss arrangements.

This instructor-led, live training in Denmark, offered online or on-site, is tailored for SRE and DevOps engineers who aim to design, build, and safely deploy AI agents for autonomous IT operations.

AI-supported compliance monitoring leverages intelligent automation to identify, enforce, and verify policy adherence throughout the software delivery lifecycle.

This live, instructor-led training (available online or onsite) is designed for intermediate-level professionals looking to embed AI-driven compliance controls within their CI/CD pipelines.

Upon completion, participants will be prepared to:

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

Course Structure

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

Customization Options

• For organizations requiring tailored compliance integrations, please reach out to us to discuss arrangements.

Leading enterprise AIOps solutions such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for identifying anomalies, correlating alerts, and automating responses within extensive IT infrastructures.

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

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

• Set up and combine Splunk, Moogsoft, and Dynatrace to create a cohesive AIOps architecture.
• Utilize AI-driven analysis to correlate metrics, logs, and events across distributed systems.
• Automate incident detection, prioritisation, and response through both built-in and custom workflows.
• Enhance performance, lower MTTR, and boost operational efficiency at an enterprise level.

Course Format

• Engaging lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation in a live-lab environment.

Customisation Options

• To arrange a tailored version of this training, please get in touch with us.

Prometheus and Grafana have become standard instruments for ensuring observability in contemporary infrastructure. By integrating machine learning, these platforms gain the ability to deliver predictive intelligence, thereby streamlining automated operational decisions. < /p>

Designed for intermediate observability specialists, this instructor-led session—available either online or onsite—focuses on evolving monitoring architectures. It guides professionals in adopting AIOps methodologies through the strategic combination of Prometheus, Grafana, and ML techniques. < /p>

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• Deploy machine learning models to identify anomalies and predict trends. < /li>
• Develop smart alerting strategies driven by predictive insights. < /li> < /ul>

  Course Structure < /p>

  • Engaging lectures complemented by open discussions. < /li>
  • Extensive practical exercises and hands-on drills. < /li>
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    Tailored Training Options < /p>

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

This instructor-led, live training in Denmark (online or onsite) targets security and ML engineers focused on identifying, testing, and defending against attacks on ML models and LLM-driven applications.