
ai-assisted-devops
Learn how DevOps Engineers can use Gen AI to enhance their productivity in day to day tasks.
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AI-Assisted DevOps is a 10-day course focusing on integrating artificial intelligence (AI) technologies into DevOps practices. The course covers various topics such as AI for observability, incident response, CI/CD pipeline optimization, security, and FinOps. Participants will learn about running large language models (LLMs) locally, making API calls, AI-powered shell scripting, and using AI agents for self-healing infrastructure. Hands-on activities include creating GitHub repositories with bash scripts, generating Docker manifests, predicting server failures using AI, and building AI agents to monitor deployments. The course culminates in a capstone project where learners implement AI-assisted DevOps automation and receive peer feedback on their projects.
README:
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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 Shell Scripting
- Mini-Challenge: "Generate a shell script to create VPC in AWS with all the best practices"
- Introduction to AIOps
- What is AIOps and What is not ?
- AI-powered monitoring with Enterprise Observability Platforms
- AIOps Recap?
- AI-powered Log Analysis
- Using AI for anomaly detection (Python)
- Demo: "Use AI to predict server failures or app 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
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