Production Machine Learning and Deep Learning for Software Engineers

As a software engineer, integrating machine learning into production requires a shift from writing traditional deterministic code to managing data, models, and pipelines. This text-based course bridges the gap by showing you how to turn raw models into resilient, scalable production services. You will learn to deploy, serve, and maintain machine learning and deep learning models using modern engineering best practices, including robust API design and automated workflows. What you'll learn: - Understand foundational machine learning and deep learning concepts, terminology, and lifecycle stages - Build and serve predictive models as scalable APIs using modern pythonic web frameworks - Containerize machine learning applications to ensure consistent environments across development and production - Implement basic CI/CD pipelines to automate the testing and deployment of your model code - Apply monitoring and maintenance strategies to track model performance and handle data drift in production The course begins with core machine learning definitions and basic model training concepts before transitioning into packaging models, building API endpoints, containerizing applications, and establishing automated deployment workflows. Designed specifically for software engineers, web developers, and system architects who are new to machine learning, this course requires no prior data science experience. Start reading today to master the engineering side of machine learning.