MLOps and Pipeline Orchestration on Cloud Platform

Transitioning a machine learning model from a local script to a reliable production environment requires more than just code; it requires a robust operational framework. This course provides a clear path for understanding how to manage the entire lifecycle of an AI project using modern cloud tools. You will learn to bridge the gap between data science and software engineering by implementing automated workflows that ensure your models remain accurate and efficient in the real world. By following this written guide, you will gain the skills to transform manual experiments into scalable, automated systems. You will learn to: * Understand the fundamental terminology and architectural patterns of MLOps. * Build automated pipelines for data processing, model training, and deployment. * Implement feature management strategies to ensure data consistency across environments. * Monitor model performance in production to detect and handle data drift. * Apply LLMOps principles for managing and scaling generative AI applications. * Practice version control for both code and datasets to ensure reproducibility. Starting with basic concepts of continuous integration and delivery for machine learning, the text-only content guides you through the complexities of feature stores, model registries, and automated retraining. You will read through detailed explanations and analyze code snippets designed to mirror real-world production scenarios. This course is designed for beginners looking to enter the field of AI infrastructure. No prior cloud experience is required, though a basic familiarity with data concepts is helpful. Begin your journey into professional machine learning operations today.