Feature Engineering for Modern Machine Learning with Scikit-Learn

1.5 Deployment Strategies for Hybrid Deep Learning Models

Once a hybrid model has been trained and validated, the deployment phase begins. This critical step requires meticulous planning to ensure the model's efficient and accurate operation in production environments, especially when dealing with multiple input types such as images and structured data. The deployment process encompasses several key aspects:

Model Optimization: This involves techniques like pruning, quantization, and compilation to reduce model size and improve inference speed without significant loss in accuracy. For instance, TensorFlow Lite can be used to optimize models for mobile and edge devices.

Infrastructure Selection: Choosing the right deployment infrastructure is crucial. ...