AI for DevOps: Integrating Intelligence into CI/CD Pipelines Training Course

AI-driven deployment orchestration is a method that leverages machine learning and automation to guide deployment strategies, detect anomalies, and initiate automatic rollbacks when necessary. This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who aim to enhance their deployment pipelines with AI-powered decision-making and resilience capabilities for government operations. Upon completing this training, participants will be able to: - Implement AI-assisted rollout strategies for safer deployments. - Predict deployment risks 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 for government can be arranged upon request.

AI-driven rollout control is an approach that leverages machine learning, pattern analysis, and adaptive decision models to manage feature flag operations and canary testing workflows for government. This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers and technical leads who aim to enhance 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 evaluate the risk associated with new feature exposure. - Automate canary analysis by utilizing performance, behavioral, and operational metrics. - Integrate intelligent scoring systems into feature flag platforms for government. - Develop rollout strategies that adapt dynamically based on real-time data. **Format of the Course** - Guided discussions supported by real-world scenarios. - Hands-on exercises focusing on 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 instructional session, delivered either online or at a physical location in US, is designed for observability and Site Reliability Engineering professionals seeking to incorporate large language models (LLMs) and artificial intelligence into their monitoring, alerting, and incident response processes. This course provides specialized knowledge for government entities and defense agencies implementing advanced AI-driven tools for security and operational efficiency within federal environments.

AIOps is a rapidly advancing field that addresses the complexities of modern IT environments, particularly those utilizing cloud architectures. The AIOps Foundation course provides a thorough introduction to the concepts, technologies, and practices associated with integrating artificial intelligence into IT operations.

This program delves into the background of AIOps, its fundamental principles, essential tools, and the organizational challenges that IT teams face when adopting these innovative approaches.

The training culminates in an exam. Successfully passing this exam awards participants the globally recognized AIOps Foundation certification, which remains valid for three years.

Who is it for?

This course is tailored 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

This comprehensive training is designed to enhance the capabilities of individuals working in these areas, providing them with the knowledge and skills necessary to effectively leverage AIOps technologies for government and other critical sectors.

Artificial Intelligence for IT Operations (AIOps) is increasingly utilized to predict incidents before they occur and automate root cause analysis (RCA) to minimize downtime and accelerate resolution. This instructor-led, live training (available online or onsite) is designed for advanced-level IT professionals who wish to implement predictive analytics, automate remediation processes, and design intelligent RCA workflows using AIOps tools and machine learning models for government. By the end of this training, participants will be able to: - Build and train machine learning 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. - Extensive exercises and practice sessions. - 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 builds upon foundational AIOps principles to offer practical, hands-on experience with real-world tools, advanced machine learning techniques, automation workflows, and operational design patterns. It equips participants to construct, configure, fine-tune, and extend AIOps pipelines and integrations.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced IT professionals who aim to develop robust AIOps ecosystems, conduct advanced analytics, and automate operational workflows using real-world tools. The course aligns with the needs of participants in various sectors, including those for government.

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

• Correlate and normalize diverse operational data sources.
• Design and refine anomaly detection and root cause analysis models.
• Integrate AIOps with IT Service Management (ITSM) and DevOps pipelines.
• Develop closed-loop automation and predictive incident workflows.

Format of the Course

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

Artificial Intelligence for IT Operations (AIOps) is the utilization of machine learning, analytics, and automation to streamline and enhance IT operations. It assists organizations in automating incident detection, correlating events, and improving operational decision-making.

This instructor-led, live training (conducted online or on-site) is designed for government IT professionals at the beginner level 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 for government.
• Explain core technologies such as big data and machine learning within AIOps contexts.
• Identify key operational metrics and use cases for AIOps in public sector environments.
• Understand the organizational impact, implementation strategies, and challenges of deploying AIOps solutions.

Format of the Course

• Interactive lecture and discussion.
• Case study review and scenario-based discussions relevant to government operations.
• Hands-on exercises with AIOps concepts and tools tailored for government IT professionals.

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

This instructor-led, live training (online or onsite) is designed for intermediate-level IT operations professionals who aim to implement AIOps techniques to correlate metrics and logs, reduce alert noise, and improve observability through intelligent automation for government agencies.

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.
• Extensive exercises and practice sessions.
• 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 constructed entirely with open-source tools enables teams to develop cost-effective and flexible solutions for observability, anomaly detection, and intelligent alerting in production environments. This instructor-led, live training (conducted online or on-site) is designed for advanced-level engineers who aim to build and deploy an end-to-end AIOps pipeline using tools such as Prometheus, ELK, Grafana, and custom machine learning models. By the end of this training, participants will be able to: - Design an AIOps architecture utilizing only open-source components. - Collect and normalize data from logs, metrics, and traces. - Apply machine learning models to detect anomalies and predict incidents. - Automate alerting and remediation processes using open tooling. **Format of the Course** - Interactive lecture and discussion. - Extensive exercises and practice sessions. - Hands-on implementation in a live-lab environment. **Course Customization Options for Government** - To request a customized training for government, please contact us to arrange.

AI-driven test generation involves a suite of techniques and tools designed to automate the creation of test cases and predict testing gaps using machine learning. This instructor-led, live training (available online or on-site) is intended for advanced-level professionals who aim to apply AI methodologies to generate tests automatically and forecast areas with insufficient coverage. Upon completing this workshop, participants will be equipped to: - Utilize AI models to develop effective unit, integration, and end-to-end test scenarios. - Analyze codebases using machine learning techniques to identify potential coverage blind spots. - Integrate AI-based test generation into continuous integration/continuous deployment (CI/CD) workflows. - Optimize test strategies based on predictive failure analytics. **Format of the Course** - Guided technical lectures complemented by expert insights. - Scenario-based practice sessions and hands-on exercises. - Applied experimentation within a controlled testing environment. **Course Customization Options for Government** - If you require this training to be tailored to your specific toolchain or workflows, please contact us to arrange.

This instructor-led, live training in US, delivered via online or onsite modalities, is designed for Site Reliability Engineering (SRE) and DevOps professionals seeking to design, develop, and securely implement artificial intelligence agents that facilitate autonomous IT operations. This program is tailored for government entities aiming to integrate advanced automation solutions into their infrastructure management workflows.

Enterprise AIOps platforms such as Splunk, Moogsoft, and Dynatrace offer robust capabilities for detecting anomalies, correlating alerts, and automating responses across large-scale IT environments. This instructor-led, live training (online or onsite) is designed for intermediate-level enterprise IT teams who wish to integrate AIOps tools into their existing observability stack and operational workflows for government. 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 Mean Time to Resolution (MTTR), and improve operational efficiency at enterprise scale. **Format of the Course** - Interactive lecture and discussion. - Extensive exercises and practice sessions. - 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 utilized tools for observability in modern infrastructure. When augmented with machine learning, these tools provide predictive and intelligent insights that enhance the automation of operations decisions. This instructor-led, live training (available online or on-site) is designed for intermediate-level observability professionals who aim to modernize their monitoring systems by integrating AIOps practices using Prometheus, Grafana, and machine learning techniques. By the end of this training, participants will be able to: - Configure Prometheus and Grafana for comprehensive observability across systems and services. - Collect, store, and visualize high-quality time series data. - Apply machine learning models for anomaly detection and forecasting. - Develop intelligent alerting rules based on predictive insights. **Format of the Course:** - Interactive lecture and discussion. - Extensive exercises and practice sessions. - Hands-on implementation in a live-lab environment. **Course Customization Options:** - To request a customized training program for government, please contact us to arrange.

This facilitated instruction-based curriculum, delivered via US (remote or in-person), is designed for machine learning engineers and platform groups responsible for developing scalable, resilient operational frameworks for large language model-driven solutions. The program emphasizes governance and efficiency for government initiatives.

This facilitated instructional session, delivered online or on-site within a localized environment, is designed for cybersecurity professionals and machine learning engineers tasked with identifying, validating, and mitigating threats targeting machine learning models and large language model (LLM) applications. This curriculum provides specialized training resources developed for government agencies to enhance operational resilience against adversarial AI attacks.