👉 Computing for complex tasks like modeling natural language, translating languages, and generating creative content demands significant computational resources. These systems require powerful GPUs or TPUs to handle the massive parallel processing needed for training deep neural networks, which consist of millions or even billions of parameters. Training these models involves iteratively adjusting weights through backpropagation, a process that can be computationally intensive and time-consuming. Additionally, real-time inference, or running models to generate outputs on user requests, necessitates efficient memory management and optimized algorithms to ensure low latency and high throughput. The entire pipeline, from data preprocessing to model deployment, often involves distributed computing across multiple machines to manage the sheer volume of data and computational load.
