The AI era demands a simple infrastructure strategy that prioritizes scalability, performance and cost efficiency in managing AI data pipelines. A key challenge is supporting large language model (LLM) training, which requires massive data, compute and storage resources. Efficient training relies on the continuous feeding of large data sets and the storage of model parameters, intermediate results and checkpoints. Above all, the infrastructure strategy must ensure that the AI resources are scalable, reliable and cost-efficient.
