This short, hands-on course helps learners adapt and optimize deep learning models for real-world use. Learners begin by exploring how transfer learning accelerates model development when data is limited. Through guided practice, they fine-tune a pretrained model, adjust freezing and unfreezing strategies, and troubleshoot common training challenges. The course then shifts to evaluating model configurations for deployment, focusing on accuracy, latency, memory footprint, and efficiency. Learners experiment with optimization methods such as hyperparameter tuning and quantization, compare multiple model setups, and make evidence-based recommendations for production environments. By the end, learners can confidently balance accuracy and performance constraints to choose the right model for their needs.