Cloud Platform AI and Machine Learning: From Data to Generative Models

Implementing machine learning in the cloud can feel overwhelming with the vast array of services, data pipelines, and model types available. This text-based guide simplifies the entire lifecycle, helping you transition from raw data to production-ready AI models. You will gain a clear understanding of how to leverage cloud-based AI services to build both predictive and generative AI solutions. By exploring real-world architectural patterns, you will learn to structure data, train models, and implement modern MLOps practices. What you'll learn: - Understand foundational AI and machine learning concepts, including supervised, unsupervised, and generative approaches. - Navigate the cloud data-to-AI lifecycle from data ingestion and preprocessing to model evaluation. - Configure predictive machine learning workflows using cloud-native tools and automated pipelines. - Explore generative AI fundamentals, including large language models and prompt engineering basics. - Understand modern Retrieval-Augmented Generation patterns and vector database integration. - Apply MLOps best practices for monitoring, deploying, and scaling models securely in cloud environments. The course starts with essential terminology and core cloud AI architecture before moving into practical data preparation and model training workflows. You will then study generative AI design patterns and modern deployment strategies through clear, structured text explanations. This course is designed for aspiring data scientists, developers, and technology enthusiasts who are new to cloud-based AI and want a solid conceptual foundation without needing prior machine learning experience. Start your journey into cloud-based artificial intelligence today.