This is a 21-day AI & GenAI learning journey specially designed for DevOps Engineers, focusing on practical concepts and real-world applications.
The course covers LLMs, GenAI, AI-powered DevOps, automation, and hands-on use cases to help DevOps engineers integrate AI into their daily workflows.
| Day | Topic | Key Concepts | Learning Material |
|---|---|---|---|
| Day 1 | 🤖 AI, ML & GenAI Basics | AI vs ML vs Deep Learning · Generative AI · LLMs · Training vs Inference · LLM Limitations |  |
| Day 2 | 🧠 How LLMs Work | Tokens · Context Window · Transformer Basics · Attention · Model Parameters · Temperature |  |
| Day 3 | ✍️ Prompt Engineering | System/User Prompts · Few-Shot Prompting · Prompt Templates · Structured Output · Prompt Chaining · Prompt Security |  |
| Day 4 | 🤖 LLM APIs | API Basics · Authentication · API Requests · Parameters · Response Handling · Error Handling |  |
| Day 5 | 🤖 Provider-Agnostic Integration Basics | Common interface across providers · Abstracting SDK differences · Config-driven provider switching · Normalizing request/response formats · Fallback between providers · Vendor lock-in risks |  |
| Day 6 | 🤖 AI Application Architecture | LLM application architecture · Context management · Memory · Tools · APIs · Guardrails |  |
| Day 7 | 🚀 Mini Project — AI DevOps Assistant | Build a simple AI DevOps Assistant · Accept a DevOps question · Generate a structured answer |  |
| Day | Topic | Key Concepts | Learning Material |
|---|---|---|---|
| Day 8 | 🔎 RAG Fundamentals | RAG · Why RAG · RAG vs LLM · RAG vs Fine-tuning · RAG Architecture |  |
| Day 9 | 📄 Documents & Ingestion | Document Loaders · Markdown · YAML · JSON · PDFs · Data Cleaning · Metadata |  |
| Day 10 | 🧩 Chunking & Embeddings | Chunking · Chunk Size · Chunk Overlap · Embeddings · Embedding Dimensions · Semantic Similarity · Cosine Similarity |  |
| Day 11 | 🗄️ Vector Databases | Vector Databases · FAISS · Chroma · pgvector · Indexing · Metadata Filtering |  |
| Day 12 | 🎯 Retrieval | Top-K · Similarity Search · Keyword vs Semantic Search · Hybrid Search · Reranking · Retrieval Quality |  |
| Day 13 | 🛠️ Building RAG | Python · Ollama · Sentence Transformers · FAISS · Retriever · Prompt Construction · Context Injection |  |
| Day 14 | 🤖 RAG Project | DevOps Release Assistant · GitHub · YAML · PRs · Commits · Docs · Embeddings · FAISS · Retriever · Ollama |  |
| Day | Topic | Key Concepts | Learning Material |
|---|---|---|---|
| Day 15 | 🤖 AI Agents | AI Agents · Agent vs RAG · Agent Workflow · Planning · Tools · Multi-step Tasks |  |
| Day 16 | 🔧 Function Calling & Tool Use | Function Calling · APIs as Tools · GitHub API · CI/CD Actions · Tool Permissions · Human Approval |  |
| Day 17 | ⚙️ AI + DevOps | AI PR Review · Commit Analysis · CI/CD Failure Analysis · Log Analysis · Deployment Troubleshooting · Release Automation |  |
| Day 18 | 🔐 AI Security | Prompt Injection · Data Leakage · Secret Management · Access Control · PII · OWASP LLM Risks · Secure Tool Execution |  |
| Day 19 | 📊 LLMOps | Model Versioning · Prompt Versioning · Evaluation · Monitoring · Logging · Cost · Latency · Observability |  |
| Day 20 | 🔌 MCP + Modern AI Architecture | MCP · MCP Server/Client · Tools vs Resources · MCP + GitHub · MCP + DevOps · Agent + RAG + MCP |  |
| Day 21 | 🚀 Final Project + Interview | AI DevOps Release Assistant · Architecture · RAG · Agents · Tools · Security · Production · Interview Preparation |  |
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