Text-to-Video AI: Designing and Training Generation Infrastructure

As generative AI expands into the realm of video, understanding the underlying compute and data infrastructure is essential for modern AI developers. This written course guides you through the complex engineering behind training advanced text-to-video models. You will transition from a general understanding of machine learning to conceptualizing the full-stack infrastructure required for large-scale video generation. You will gain a clear mental map of how data pipelines, distributed training clusters, and evaluation frameworks cooperate to train state-of-the-art diffusion and transformer-based models. What you will learn: Understand the foundational concepts of diffusion models and transformer architectures used in modern video generation; Explore data processing pipelines designed to handle, clean, and format massive video-text datasets; Learn the principles of distributed training, including data and tensor parallelism, to scale training across large compute clusters; Analyze infrastructure configurations and orchestration strategies for managing high-performance workloads; Evaluate model performance using standard metrics and modern alignment techniques. The course begins with core terminology and foundational definitions of video generation systems. You will then progress step-by-step through data ingestion, distributed training setups, and system evaluation using comprehensive written explanations and architectural walkthroughs. This course is designed for aspiring AI engineers, infrastructure enthusiasts, and tech professionals looking for a beginner-friendly entry point into generative AI systems, with no advanced prerequisites required. Start building your foundational knowledge of next-generation AI infrastructure today.