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Why AI is a game-changer for DevOps
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Overview of Generative AI and LLMs (without deep theory)
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Popular AI tools for DevOps Engineers.
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Hands-on: Create a GitHub repository that contains a bash script. when executed the bash script confirms the health of a virtual machine by looking at the parameters such as cpu, disk space, memory e.t.c.,. Please note that the bash script should also support a command line argument named "explain", when passed, "explain" provides the detailed summary of the health status.
- Try the hands-on demonstration explained in the video.
- Fundamentals: Tokens, temperature and max tokens.
- Techniques: Zero-shot, few-shot, n-shot, and Chain-of-Thought (CoT) prompting
- Writing structured prompts for DevOps use cases
- AI-generated regex, Bash scripts, Terraform, and CI/CD configurations
- Live Demo: Demonstrate an example of few shot prompting in real time.
- Running LLMs locally (Ollama, LM Studio, GPT4All)
- Calling AI via APIs (OpenAI, Mistral, LLama, Deepseek e.t.c.,.)
- Python script to invoke ollama api
- Dockerfile Generation "Call Ollama endpoint to auto-generate Docker manifests using llama3 model"
- "Call an AI API to auto-generate Kubernetes manifests using llama3 model"
- Using AI to improve Bash/Python scripting
- AI-assisted log analysis and troubleshooting
- Mini-Challenge: "Generate a log analysis script using AI & refine it for better accuracy"
- AI-powered monitoring with Prometheus, Grafana, and New Relic
- AI for log pattern recognition & anomaly detection
- Mini-Challenge: "Use AI to analyze a set of logs and summarize root causes"
- What is AIOps and how does it work?
- AI-powered incident detection and auto-remediation
- Using AI for predictive maintenance & anomaly detection
- Tools: Dynatrace, Moogsoft, IBM Watson AIOps
- Live Demo: Running AIOps-based incident detection & root cause analysis
- Mini-Challenge: "Use AI to predict server failures based on logs"
- AI-powered automation in Jenkins, GitHub Actions, GitLab CI/CD
- AI-assisted YAML validation and error fixing
- Mini-Challenge: "Generate a GitHub Actions pipeline using AI and debug an error"
- What are AI Agents? How do they work?
- AI-powered self-healing infrastructure
- Project: Build a simple AI agent that monitors a deployment and suggests fixes
- AI-assisted vulnerability scanning (Trivy, Snyk, Checkov)
- AI-powered cloud cost optimization (FinOps)
- AI-generated compliance reports (CIS, NIST, PCI-DSS)
- Mini-Challenge: "Use AI to scan a container image for vulnerabilities"
- Live Demo: Running an AI-powered cloud cost analysis
- AI trends in DevOps (AI-powered SRE, AIOps, FinOps)
- Final Capstone Project: Implement an AI-assisted DevOps automation
- Peer Review: Learners give feedback on each other's projects