Serverless Keras Model Deployment on AWS and Google Cloud

Deploying deep learning models can be complex and expensive when managing traditional server infrastructure. Serverless architectures offer a scalable, cost-effective alternative for serving machine learning models on demand.\n\nThis course guides you through the entire process of preparing, packaging, and deploying your Keras models to the cloud using serverless functions. You will learn how to optimize your models, handle dependencies, and configure cloud environments to serve real-world predictions efficiently.\n\nWhat you'll learn:\n- Understand the foundational concepts of serverless computing and its benefits for machine learning\n- Prepare and export Keras models for efficient cloud-based inference\n- Package models and dependencies using modern containerization to overcome size limitations\n- Deploy serverless functions on AWS Lambda and Google Cloud Functions\n- Configure memory, timeouts, and scaling resources for optimal performance and cost\n- Set up secure API endpoints to handle prediction requests\n\nYou will start with key terminology and the core mechanics of serverless architecture before walking through step-by-step deployment workflows on both AWS and Google Cloud.\n\nThis course is designed for beginners and intermediate learners who want to bridge the gap between model training and cloud deployment. No prior cloud engineering experience is required, though basic familiarity with Python and Keras is recommended.\n\nRead on to master modern serverless deployment and make your deep learning models accessible to the world.