Building and Deploying Keras ML Models on Cloud Infrastructure

Transitioning from local machine learning experiments to production-ready cloud deployments can feel overwhelming. This text-based course bridges that gap by guiding you through the essential steps of building, tuning, and deploying models using Keras and TensorFlow on modern cloud infrastructure. You will progress from understanding foundational machine learning concepts to managing full-lifecycle deployments. By studying clear written explanations and structured code snippets, you will gain the confidence to scale your models, optimize their accuracy, and serve predictions efficiently using cloud-based endpoints. What you'll learn: - Learn core machine learning principles and Keras API fundamentals. - Build and train deep learning models using TensorFlow. - Optimize model performance through hyperparameter tuning and regularization. - Deploy trained models to cloud endpoints for real-time predictions. - Understand modern MLOps concepts including model versioning and monitoring. The curriculum starts with foundational ML concepts and local Keras workflows before transitioning into cloud-based training and scalable deployment strategies. You will explore practical code examples that demonstrate how to handle data pipelines and manage model lifecycles in production. This course is designed for aspiring data scientists, software developers, and beginners eager to learn cloud-based machine learning. No prior experience with cloud platforms or advanced deep learning is required, though a basic familiarity with Python is helpful. Start reading today to master the journey from local code to cloud-deployed machine learning models.