Cloud Machine Learning with TensorFlow and Keras

Harnessing the power of machine learning requires more than just writing code; it demands the ability to scale your models using cloud infrastructure. This text-based course guides you through the fundamentals of designing, training, and deploying machine learning models using TensorFlow and Keras on the cloud. You will transition from understanding basic neural network concepts to managing cloud-based machine learning workflows. By reading detailed explanations and analyzing practical code examples, you will learn how to optimize model performance, scale training workloads, and deploy your models as accessible cloud services. What you'll learn: - Understand foundational machine learning concepts, neural network architectures, and the TensorFlow ecosystem. - Build and train predictive models using the intuitive Keras API. - Prepare and pipeline data efficiently using modern data loading techniques. - Scale model training utilizing cloud-based computational resources. - Deploy trained models to cloud endpoints for real-time predictions. - Apply modern MLOps principles to monitor and maintain model performance over time. The course begins with core machine learning definitions and TensorFlow basics before moving into hands-on model architecture with Keras. You will then explore cloud integration, focusing on data pipelines, distributed training, and deployment strategies. Designed for beginners in machine learning and cloud development, this course requires no prior experience with TensorFlow or cloud platforms, though basic Python familiarity is helpful. Start your journey into cloud-based machine learning today.