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Meet the Expert : Dean Wampler on Scaling ML/AI Applications with Ray /
Modern ML and AI applications require a lot of compute power, which usually means distribution over a cluster of machines, as well as management of distributed state, such as the model parameters being trained. Ray, a high-performance distributed execution framework developed by UC Berkeley's R...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
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[Place of publication not identified] :
O'Reilly Media, Incorporated,
2020.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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| 520 | |a Modern ML and AI applications require a lot of compute power, which usually means distribution over a cluster of machines, as well as management of distributed state, such as the model parameters being trained. Ray, a high-performance distributed execution framework developed by UC Berkeley's RISELab, is targeted at large-scale machine learning and reinforcement learning applications. Ray's features make it suitable for any Python-based application that needs cluster-wide scalability. Join us for this edition of Meet the Expert with Dean Wampler to see how Ray meets the needs of ML/AI applications-without requiring the skills and DevOps effort typically required for distributed computing. You'll learn how Ray enables distribution of Python applications over a cluster and explore examples of ML libraries that use Ray, allowing data scientists to do their work at scale without a lot of programming. O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You'll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions. | ||
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