Deploying ML Models: Web Services and Model Persistence

Training a machine learning model is only half the battle; the real value comes when you deploy it so others can use it. Transitioning from a local environment to a production-ready web service requires understanding model persistence and API design. This course guides you through the entire process of saving your trained models and serving them as reliable, scalable web endpoints. You will gain the confidence to bridge the gap between data science and software engineering using modern industry standards. What you'll learn: - Understand the fundamentals of model persistence, serialization, and deserialization using modern formats. - Build robust web APIs using FastAPI to serve model predictions in real-time. - Configure environment variables and dependency management for consistent deployments. - Apply containerization basics to package your model services for cloud environments. - Test your deployed endpoints using structured written scenarios and mock requests. - Design secure and efficient model pipelines that handle incoming web traffic reliably. You will start by learning the core terminology of model serialization and web protocols. From there, you will progress through step-by-step written explanations on designing APIs, managing dependencies, and containerizing your services, concluding with written self-assessment exercises to test your knowledge. This course is designed for beginner data scientists, software developers, and aspiring MLOps engineers who have a basic understanding of Python and want to learn how to deploy their models. No prior deployment or cloud experience is required. Start reading today and take your first step toward mastering machine learning deployment.