Understanding the AI Data Pipeline
Автор: Solidigm
Загружено: 2024-09-10
Просмотров: 583
The AI boom is creating massive global scalability challenges and cannot be sustained without significant improvements to operational efficiency. Legacy infrastructures will soon hit a scalability ceiling. The time has come to replace existing HDD infrastructures with SSDs, accelerating and transforming infrastructure to support AI workloads.
Success of AI models is largely dependent on dataset size, training performance and overall efficiency of the AI data pipeline. In the last few years, the term GPU has evolved from tech speak to party conversation as enterprises of all sizes clamor to get on board the AI train. One major challenge in AI adoption is leveraging expensive GPUs efficiently to boost overall performance.
Inefficient storage can decrease GPU utilization, increase power usage throughout your AI infrastructure, require more physical space for the same capacity, and substantially hinder model training speed.
Whether you're a data architect, AI researcher, or tech enthusiast, this video will give you insights into the critical role of efficient and reliable storage in the AI data pipeline.
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