Production Machine Learning for Software Engineers

As a software developer, writing clean code is second nature, but transitioning to machine learning requires a fundamental shift in how you build and maintain systems. This text-only course bridges the gap between software engineering and data science, teaching you how to operationalize and manage machine learning models in production environments. You will start by mastering foundational machine learning concepts and terminology before moving on to practical integration workflows. By reading through clear explanations and structured code examples, you will learn how to turn raw models into reliable, production-ready services. What you'll learn: - Understand core machine learning concepts, pipeline stages, and terminology. - Build and evaluate predictive models using modern Python libraries. - Package machine learning models into reproducible environments using Docker. - Deploy models as scalable, high-performance web APIs with FastAPI. - Implement basic monitoring strategies to track model performance and data drift. - Apply testing and version control best practices to machine learning code. This course begins with essential concepts and vocabulary before guiding you through model packaging, API development, and deployment pipelines. It is designed specifically for software engineers and developers who want to add machine learning engineering to their skill set, with no prior data science background required. Begin building and deploying production-ready machine learning systems today.