The Model Evaluation and Benchmarking 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 equips learners with the skills to assess and compare the performance of both text and image generative models. Starting with text evaluation, learners apply standard metrics such as perplexity, BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), and BERTScore, while also designing human evaluation protocols and task-specific methods for applications like summarization or translation. The course then explores image evaluation using technical metrics, including FID (Fréchet Inception Distance), CLIP similarity (Contrastive Language–Image Pretraining similarity), and SSIM (Structural Similarity Index Measure), alongside human perception-based assessment techniques and artifact detection systems. In the final module, learners design comprehensive benchmarking frameworks with reproducible testing environments, version control, and visualization dashboards for continuous monitoring. By the end, learners will be able to implement automated, domain-specific evaluation systems and deliver detailed performance reports that ensure generative models meet rigorous quality standards.