Preparing Images for AI Models

The Preparing Images for AI Models course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course provides learners with essential skills to source, prepare, and augment image datasets for training diffusion models. Learners begin by navigating public repositories such as the Large-scale Artificial Intelligence Open Network (LAION), ImageNet, and Flickr30k, evaluating datasets for quality, diversity, and legal compliance. The course then introduces preprocessing workflows, including resizing, cropping, normalization, and metadata management to enhance dataset consistency. Learners practice batch processing for large collections while applying quality checks to detect corrupted or duplicate files. The final module focuses on augmentation strategies—ranging from basic transformations to advanced techniques like CutMix, MixUp, and style transfer—to improve robustness and diversity without introducing distribution shifts. By the end of the course, learners will have developed a structured, production-ready dataset optimized for training or fine-tuning diffusion models.