Machine Learning with TensorFlow on the Cloud

Scaling machine learning models from a local environment to the cloud is a critical skill for modern developers and data scientists. Transitioning your workflows to cloud infrastructure allows you to train larger models and serve predictions efficiently. In this text-based course, you will learn how to design, train, and deploy machine learning pipelines using TensorFlow on cloud platforms. You will start with core machine learning concepts and progress to building production-ready models using scalable cloud resources. What you'll learn: - Understand foundational machine learning concepts, terminology, and data preprocessing workflows. - Build and train neural networks using TensorFlow and Keras APIs. - Configure scalable data pipelines using TensorFlow tools designed for large datasets. - Deploy trained models to cloud endpoints for real-time and batch predictions. - Apply basic MLOps principles to monitor, version, and manage your cloud-hosted models. The course guides you step-by-step through setting up your environment, writing clean TensorFlow code, and leveraging cloud services to scale your training and deployment. You will study practical, written explanations and code snippets designed to build your confidence. This course is designed for beginners who want to transition their machine learning skills to the cloud. No prior cloud experience is required, though a basic understanding of Python is helpful. Start reading today to take your machine learning models to the cloud.