AI for SRE & DevOps: A Practical Guide to AIOps

Build intelligent, reliable systems using AI, AIOps, and real-world SRE practices

What you’ll learn
✓ Learn the fundamentals of AI and Machine Learning
✓ How to apply AI/ML to SRE, DevOps, and Infrastructure operations
✓ Understand why AI is important for modern SRE
✓ Build an AI-enabled SRE workflow by integrating AI tools into CI/CD pipelines
✓ Understand the challenges and limitations of AI in SRE
✓ Prepare for next-generation SRE roles

Requirements
● Basic understanding of IT systems and interest in SRE, DevOps, Infrastructure, or Cloud Engineering
● No prior experience in AI or Machine Learning is required. All AI and ML concepts are explained in simple English, starting from the basics and gradually moving to practical, real-world examples.

Description
Modern IT systems are more complex than ever. Cloud platforms, microservices, Kubernetes, CI/CD pipelines, and 24×7 availability expectations have made reliability and operations a critical challenge. Traditional monitoring and manual operations are no longer enough. This is where AI-powered SRE (AIOps) plays an important role.

This course teaches how Artificial Intelligence can be practically applied to Site Reliability Engineering (SRE), DevOps, and Infrastructure operations. Everything is explained in simple English, starting from the basics and gradually moving to real-world use cases. No prior knowledge of AI or Machine Learning is required.

You will begin by learning core SRE concepts such as SLIs, SLOs, SLAs, error budgets, monitoring, observability, and incident management. Then you will understand the fundamentals of AI and Machine Learning and why they are relevant for modern operations teams.

The course covers practical applications of AI such as intelligent log analysis, anomaly detection, alert noise reduction, predictive alerting, and root cause analysis. You will also learn how AI improves infrastructure operations, including predictive scaling, capacity forecasting, cloud cost optimization, and Kubernetes autoscaling.

In addition, the course explains AI in change and release management, AI-enabled SRE workflows, security and ethics, and the future of AI in SRE. Hands-on demos using simple Python scripts and popular tools like Grafana and Elastic help you connect theory with practice.

By the end of this course, you will have a clear understanding of how to design and work with AI-powered SRE systems and prepare yourself for next-generation SRE and DevOps roles.

Who this course is for
■ SRE and DevOps Engineers looking to apply AI and AIOps concepts in CI/CD pipelines and daily operations
■ Infrastructure and Cloud Engineers who want to use AI for predictive scaling, cost optimization, and capacity planning