Cloud Machine Learning: Build, Train, and Deploy with Keras and Vertex AI

Implementing machine learning in production requires more than just writing model code; it demands scalable cloud infrastructure and robust pipeline automation. This text-based course guides you through the entire lifecycle of machine learning, from local prototyping to cloud-scale deployment. You will transition from writing simple neural networks to orchestrating managed machine learning pipelines. By studying clear written explanations and structured code walk-throughs, you will gain the practical skills needed to prepare datasets, train models with Keras, and serve predictions reliably in the cloud. What you'll learn: Understand foundational machine learning concepts and cloud AI terminology; Build and train deep learning models using TensorFlow and Keras; Prepare and ingest large datasets efficiently for cloud-based training workflows; Deploy trained models to Vertex AI for real-time and batch predictions; Implement modern MLOps practices to monitor and manage your model pipelines. The course begins with core machine learning definitions and Keras basics before progressing to cloud data ingestion, model training, and production deployment strategies on Vertex AI. You will practice through structured written exercises and step-by-step code analysis. This course is designed for aspiring data scientists, developers, and cloud enthusiasts who are new to machine learning deployment. No prior cloud experience is required, though a basic understanding of Python is helpful. Start reading today to master the essentials of cloud-based machine learning workflows.