Bookmarks Meet the Expert: Dean Wampler on Scaling ML/AI Applications with Ray

Meet the Expert: Dean Wampler on Scaling ML/AI Applications with Ray
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Meet the Expert: Dean Wampler on Scaling ML/AI Applications with Ray
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Meet the Expert: Dean Wampler on Scaling ML/AI Applications with Ray| Size: 1.76 GB

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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