Machine Learning Model Deployment with Python and Docker

Many aspiring data scientists can build high-performing machine learning models in a local environment, but struggle to share those models with the rest of the business. Bridging the gap between data science and software engineering is the key to delivering real business value. This text-based course guides you through the entire lifecycle of model deployment. You will learn how to take raw machine learning, natural language processing (NLP), and deep learning models, wrap them in clean web APIs, and package them into lightweight Docker containers that can run reliably anywhere. What you'll learn: - Understand foundational containerization concepts and write efficient Dockerfiles - Build robust web APIs using Flask and modern frameworks like FastAPI to expose your models - Deploy a supervised Random Forest model to handle real-time prediction requests - Package an NLP clustering model and a deep learning image classification model for production - Apply modern MLOps best practices to manage dependencies, environment variables, and container lifecycles Starting with basic definitions of APIs and containers, the material walks you through step-by-step written explanations and practical code implementations, moving from simple regression models to complex neural networks. This course is designed for beginner data scientists, Python developers, and software engineers looking to expand their skills into model deployment and basic DevOps. No prior containerization experience is required. Start reading today to transform your local machine learning code into scalable, production-ready web services.