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AI/ML Engineering

AI/ML Engineering

Phase 0: Prerequisites

Phase 0: Prerequisites Prerequisites & Environment Setup

Phase 1: AI-Native Development

Phase 1: AI-Native Development AI Coding Tools Landscape Local Models for AI Coding Claude Code & CLI Deep Dive Agent-First IDEs CLI AI Coding Agents Prompt Engineering Fundamentals AI-Powered Code Generation AI-Assisted Debugging & Optimization Building with AI Coding Assistants

Phase 2: Generative AI

Phase 2: Generative AI Introduction to Large Language Models Tokenization & Text Processing Text Generation & Sampling Strategies Embeddings & Semantic Search Vector Space Visualization Reasoning Models: System 2 Thinking

Phase 3: Vector Search & RAG

Phase 3: Vector Search & RAG Vector Databases Deep Dive Building RAG Systems Advanced RAG Patterns RAG Evaluation & Optimization Long-Context LLMs and Prompt Caching

Phase 4: Frameworks & Agents

Phase 4: Frameworks & Agents LangChain Fundamentals LangChain Advanced LangGraph for Agents LlamaIndex Building AI Agents Agent Memory & Planning Multi-Agent Systems Model Context Protocol (MCP) for Agents Computer Use and Browser Automation Agents Next-Gen Agentic Frameworks

Phase 5: MLOps & LLMOps

Phase 5: MLOps & LLMOps ML DevOps Foundations Docker for ML CI/CD for ML Kubernetes for ML Advanced Kubernetes Experiment Tracking Data Pipelines ML Pipelines Model Serving ML Monitoring

Phase 6: AI Infrastructure

Phase 6: AI Infrastructure Cloud AI Services AIOps High-Performance LLM Inference: vLLM and sglang

Phase 7: Advanced GenAI & Safety

Phase 7: Advanced GenAI & Safety Fine-tuning LLMs LoRA & Parameter-Efficient Fine-tuning Diffusion Models RLHF & Alignment Advanced Generation Techniques LLM Evaluation AI Red Teaming AI Safety & Alignment Modern PEFT: DoRA and PiSSA

Phase 8: Multimodal AI

Phase 8: Multimodal AI Voice & Audio AI Vision AI Video AI Multimodal-First AI Design

Phase 9: Deep Learning Foundations

Phase 9: Deep Learning Foundations Neural Network Fundamentals PyTorch Fundamentals Training Neural Networks CNNs & Computer Vision RNNs & Sequence Models Backpropagation Deep Dive Transformers from Scratch

Phase 10: Classical ML

Phase 10: Classical ML Scikit-learn & Classical ML XGBoost & Gradient Boosting Time Series Forecasting

Appendix A: History of AI/ML

Appendix A: History of AI/ML History of AI & Machine Learning

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Overview
Modules

On this page

Overview
Modules

Phase 6: AI Infrastructure

AI/ML Engineering Track | Phase 6

Modules

Section titled “Modules”

#	Module
1.1	Cloud AI Services
1.2	AIOps
1.3	High-Performance LLM Inference: vLLM and sglang

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