11. Embeddings Explained | Production AI Engineering

Learn how to deploy machine learning and AI applications from a Jupyter Notebook to a production-ready system. This complete 18-part AI Production Engineering course covers every stage of the deployment pipeline, including MLflow experiment tracking, model registry, FastAPI APIs, Gradio interfaces, ONNX optimization, vLLM inference, embeddings, FAISS vector search, Weaviate, Retrieval-Augmented Generation (RAG), and Graph RAG. Every lesson is built from real, executable Python code with reproducible outputs. You'll learn not only how each technology works, but also how they connect together to build scalable AI systems used in modern production environments. By the end of the course, you'll build a complete end-to-end AI application that starts with a trained machine learning model and finishes with a production-ready Graph RAG system capable of semantic search and knowledge-aware retrieval. Course Notebook https://github.com/kader-xai/ml-cours... If you enjoy this course, these playlists are a great next step: Machine Learning Series    • Machine Learning Series   Scikit-Learn Series    • SciKit Learn Series   Machine Learning from Scratch    • Machine Learning from Scratch   Data Science with Python    • Data Science with Python   AI Agents with LangGraph    • AI Agents with LangGraph   XGBoost for CyberDefense    • XGBoost for CyberDefense   Neural Network Optimization    • Neural Network Optimization   Hugging Face Transformers    • Hugging Face Transformers   PyTorch: Build Your Own GPT    • Pytorch : Build your own GPT   TensorFlow from Scratch    • Tensor Flow from scratch   Course Structure PACKAGE 01. From Notebook to Service 02. The Model Artifact 03. MLflow Experiment Tracking 04. MLflow Model Registry SERVE 05. FastAPI Model Serving 06. FastAPI Advanced APIs 07. Building AI Interfaces with Gradio OPTIMIZE 08. Exporting Models to ONNX 09. ONNX Runtime Optimization 10. High-Performance Inference with vLLM RETRIEVE 11. Embeddings Explained 12. Vector Search with FAISS 13. Scaling FAISS 14. Weaviate Vector Database GRAPH RAG 15. Retrieval-Augmented Generation (RAG) 16. From RAG to Graph RAG 17. Graph RAG Retrieval Pipeline 18. End-to-End AI Production Capstone Subscribe for more AI Engineering, Machine Learning, Deep Learning, LLM, and Data Science courses.