Data Science Model Deployment and Cloud Computing on GCP

Transitioning a data science model from a local notebook to a reliable production environment is one of the most critical steps in any modern AI project. This course provides a clear, step-by-step path to understanding how cloud infrastructure supports machine learning models in the real world. You will transition from writing local code to deploying scalable, secure, and monitored machine learning APIs on GCP, gaining the confidence to manage cloud resources and implement modern deployment workflows. What you'll learn: - Understand foundational cloud computing concepts and GCP architecture for data science. - Package machine learning models using modern containerization tools like Docker. - Deploy models as web APIs using scalable cloud services. - Implement basic MLOps pipelines for automated model deployment and versioning. - Monitor model performance and handle predictions in real-time and batch environments. The course begins with essential terminology, cloud fundamentals, and model packaging concepts before guiding you through practical deployment scenarios and continuous monitoring strategies. You will read structured explanations, analyze architectural patterns, and work through step-by-step written deployment exercises. This course is designed for beginner data scientists, software engineers, and analysts looking to step into cloud deployment, with no prior cloud experience required. Start reading today to bridge the gap between data science theory and cloud production.