The Fundamentals of Efficient AI Workload Management
Bibliographic Data
| ID | 23593816 |
|---|---|
| Authors | Ishneet Kaur Dua (0009-0002-2928-8282), Parisa Patel (0000-0003-0053-3635), Parth Girish Patel (0009-0003-7800-9486) |
| Year | 2024 |
| Pages | 39-56 |
| Publication date | 2024-01-01 |
| Open Access | Yes |
| Type | CHAPTER |
| Venue | Optimizing Generative AI Workloads for Sustainability (SOURCE_BOOK) |
| Publisher | Apress (PUBLISHER • US) |
| DOI | 10.1007/979-8-8688-0917-0_2 |
| OpenAlex | W4404480370 |
| ISBN | 9798868809170 |
| Language | EN |
There has been a surge in the demand for efficient and scalable artificial intelligence (AI) workloads. As AI models continue to increase in complexity and size, managing and optimizing the computational resources required for training and deploying these models has emerged as a critical challenge. This chapter aims to elucidate the essential principles and practices that enable organizations to effectively harness the power of AI while maximizing resource utilization and minimizing operational costs.
Operating system · Workload · Business Process Modeling and Analysis · Computer Science
| Highly cited | No |
|---|