Deploying Scalable Machine Learning Models with Python and Flask

Building a great machine learning model is only half the battle; the real value comes when you make it accessible to others. This text-based course guides you through the process of wrapping your models in stable, scalable web services. You will transition from running local scripts to designing robust web APIs. By understanding the core mechanics of web servers and model serialization, you will gain the skills to deploy accessible machine learning endpoints capable of handling real-world traffic. What you'll learn: Understand the foundational concepts of web services, HTTP requests, and RESTful API design; Build clean API endpoints using Python and Flask to serve model predictions; Manage project dependencies and environments efficiently using modern tools like Poetry; Configure production-ready application servers using Gunicorn to handle concurrent traffic; Write automated unit tests for your API endpoints using pytest to ensure reliability; Prepare your application for cloud deployment using standard containerization concepts. The course begins with foundational web concepts and environment setup, then moves systematically through API creation, model integration, testing, and production scaling. You will learn through clear text explanations and practical code examples designed for immediate application. This course is designed for beginner data scientists, machine learning enthusiasts, and software developers who want to learn deployment basics. A basic understanding of Python is recommended, but no prior web development or DevOps experience is required. Start transforming your local models into active web services today.