Custom TF Lite Models with Model Maker

Deploying machine learning models to mobile and resource-constrained devices requires special optimization. This text-only course guides you through the process of building custom, lightweight models using TF Lite Model Maker. You will start with the fundamental concepts of edge AI, transfer learning, and dataset preparation. By reading through our structured explanations and practicing with clear code examples, you will learn how to adapt pre-trained models to your own datasets, apply optimization techniques like quantization, and prepare models for deployment on mobile and embedded systems. What you'll learn: * Understand the core principles of edge computing and lightweight machine learning models * Prepare and format custom datasets for transfer learning tasks * Train custom classification models efficiently using TF Lite Model Maker * Apply post-training quantization to reduce model size and improve performance on edge devices * Evaluate model accuracy and troubleshoot common training issues * Export optimized model files ready for integration into mobile applications. The course begins with foundational definitions and data preparation basics before moving step-by-step through training, optimization, and export. It is designed for beginners and developers looking to transition their machine learning skills to edge devices, with no prior deep learning experience required. Start your journey into edge AI today.