What you'll learn

Training a deep learning model is just the beginning. The real challenge is deploying it so that it can serve predictions to real users and applications. In this course, Deep Learning Model Deployment, you’ll gain the ability to take trained models from development into production environments as reliable, scalable services. First, you’ll explore how to prepare models for deployment by serializing them in portable formats, optimizing their size and speed, and packaging them with dependencies. Next, you’ll discover how to build RESTful APIs for model inference, containerize services with Docker, and deploy them to production environments. Finally, you’ll learn how to monitor model performance over time, detect drift, and implement automated maintenance workflows. When you’re finished with this course, you’ll have the skills and knowledge of deep learning deployment needed to transform your trained models into production-ready services that deliver real-world value.