Explore comprehensive strategies and best practices for deploying AI models, including real-time serving, edge deployment, and using tools like TensorFlow Serving. Learn about containerization, scaling, versioning, and MLOps for efficient AI model deployment.
Explore design patterns for integrating machine learning into Java applications, including data pipelines, model serving, and feature stores, with practical examples and best practices.