AI BootCamps (6 Courses)
Career Oriented
Course Category Career Oriented
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Learners
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Enterprises
By 2029, 70% of enterprises will deploy agentic AI in IT infrastructure operations, up from less than 5% in 2025. For DevOps engineers and SREs, this is not a distant trend; it is already reshaping how pipelines are built, incidents are resolved, and infrastructure is provisioned. This 16-hour live course applies Gen AI and Agentic AI across 12 DevOps domains: CI/CD pipeline design, infrastructure-as-code, containerisation and Kubernetes, cloud cost optimisation, observability and monitoring, incident response, security and compliance, release management, SRE practices, GitOps, platform engineering, and documentation with both AI levels in every session: Gen AI to work faster, Agentic AI to work autonomously.
You leave knowing how to build incident response agents that detect, triage, and remediate without waiting for human intervention; how to use AI to generate and review IaC at speed; and how to govern AI-generated infrastructure with the guardrails that keep production environments safe, the exact skills that separate the DevOps engineer of today from the AI orchestrator this role is becoming.
The full DevOps lifecycle with Gen AI and Agentic AI applied at every stage
Incident response agents from anomaly detection to runbook execution to Slack notification, fully autonomous
AI-generated IaC, Terraform, Kubernetes manifests, and pipeline YAML generated, reviewed, and validated with AI
Security and governance coverage: how to validate AI-generated infrastructure and enforce policy guardrails
24 PDUs and 24 SEUs valid for renewal
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Use AI to design, evaluate, and document infrastructure decisions faster and with greater rigour than manual analysis allows.
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Use AI to build faster, more reliable CI/CD pipelines reducing build times, improving failure detection, and automating pipeline maintenance.
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Use AI to detect, diagnose, and resolve incidents faster, reducing mean time to resolution and improving post-incident learning.
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Use AI to strengthen security posture, automate compliance checks, and surface vulnerabilities before they become incidents.
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Use AI to build smarter monitoring, reducing alert noise, detecting real issues faster, and understanding system behaviour more deeply.
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Use AI to write, review, and maintain Infrastructure-as-Code faster and with fewer configuration errors.
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Use AI to plan, execute, and document releases more safely, reducing deployment risk and accelerating recovery when things go wrong.
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Use AI to create and maintain the technical documentation that teams always need but rarely have time to write properly.
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Use AI to identify cloud cost waste, optimise resource allocation, and build cost awareness into engineering practice.
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Use AI to improve how the DevOps team works, running better retrospectives, tracking improvement actions, and using DORA metrics intelligently.
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Use AI to make on-call more manageable, improve system reliability, and reduce the operational burden on engineering teams.
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Apply every skill from the course to a realistic end-to-end DevOps scenario across infrastructure, delivery, incidents, and reliability.
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To fast-track your career and achieve
This course helped me automate half my weekly tasks. Gen AI + DevOps is a game-changer. Thank you Agilefever!
Hands-on, straight to the point, and full of real-world use cases. Highly recommend this for any DevOps pro.
The instructors were solid. I now use AI tools daily at work, and my manager noticed the difference!
No — DevOps roles are shifting from execution to orchestration. AI handles repetitive toil; engineers govern the agents, set policy guardrails, and handle the novel failures that AI cannot. Gartner, McKinsey, and DevOps.com all project the same outcome: AI augments the role, raises its strategic value, and commands higher pay for those who adapt.
Not at all but ML background required. The AI Foundations course (4 hours) is the only prerequisite, and it covers everything you need. This course is designed for working DevOps engineers — it assumes you know pipelines, cloud infrastructure, and terminal, not AI research.
It is real and in production. Azure’s SRE Agent, AWS CloudWatch’s anomaly detection, and MCP-powered multi-agent systems are deployed at enterprise scale today. A typical agent detects high CPU, checks logs, correlates it to a recent deployment, rolls back, updates Jira, and posts to Slack — all within minutes. The course teaches you how to design, configure, and govern these workflows, not just read about them.
The course is cloud-agnostic by design — principles and patterns that work across AWS, Azure, and GCP. Tools covered include GitHub Copilot, Claude for DevOps, ChatGPT, and examples from CloudWatch, Azure Monitor, and Terraform. The techniques taught transfer regardless of which platform your team runs on.
This is exactly what the course addresses. AI-generated infrastructure introduces real risks — misconfigured IAM policies, open security groups, resource drift — and the course covers how to review, validate, and test AI-generated IaC before it touches production. The governance and guardrail skills taught here are what separate responsible AI adoption from chaos-at-speed.
Both. The curriculum covers SRE-specific topics — incident response, observability, reliability patterns, runbook generation, and post-incident documentation — as well as CI/CD, IaC, and platform engineering. SREs will find the incident response and monitoring modules particularly high-value; pipeline-focused engineers will get the most from the CI/CD and GitOps sessions.
Traditional automation follows scripts you write and rules you define. Agentic AI pursues goals: it reads context, decides what to do, takes multi-step actions, and adapts when conditions change — without you initiating each step. A script restarts a service when you tell it to. An agent detects that the service is degraded, diagnoses the cause, decides whether to restart or roll back, executes the remediation, and documents what it did.
AIOps is the application of AI and ML to IT operations — and yes, much of what this course covers falls under that umbrella. You will learn the practical implementation layer: how to use AI for log analysis, anomaly detection, predictive failure, and auto-remediation. The course focuses on what a working DevOps engineer can apply now with available tools, not on building custom ML models.
Yes — Kubernetes is covered both in the containerisation module (AI-assisted manifest generation and review) and the incident response module (AI agent diagnosis in K8s environments, including the memory pressure and connection pool patterns that are the most common production failure modes). The course assumes you already know Kubernetes fundamentals and focuses specifically on how AI changes the way you work with it.
PDUs count toward PMP and PMI renewal under Technical Education. SEUs count toward SAFe DevOps Practitioner and Scrum Alliance credentials. DevOps Institute DASA certifications also accept continuing education in AI and automation. Check with your specific certification body, but for most DevOps and SAFe credentials this course qualifies directly.