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Viraat Aryabumi

Research Scholar

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Viraat is a Research Scholar at Cohere for AI, focusing on cutting-edge AI research. He played a pivotal role in developing the Aya Project and the subsequent release of the Aya-101 model. Before his current role, Viraat was the head of Machine Learning and AI at Aiara, a pioneering AI-for-manufacturing startup. Before his tenure at Aiara, Viraat distinguished himself as a Machine Learning Scientist at the Amazon Development Centre in Scotland. During his time at Amazon, he specialized in Display Advertising, where he honed his expertise in building and optimizing deep learning models within world-scale data systems, all while ensuring low latencies for optimal user experience. Viraat's academic journey includes obtaining a Master's in Artificial Intelligence from the prestigious School of Informatics at the University of Edinburgh.Viraat is a Research Scholar at Cohere for AI, focusing on cutting-edge AI research. He played a pivotal role in developing the Aya Project and the subsequent release of the Aya-101 model. Before his current role, Viraat was the head of Machine Learning and AI at Aiara, a pioneering AI-for-manufacturing startup. Before his tenure at Aiara, Viraat distinguished himself as a Machine Learning Scientist at the Amazon Development Centre in Scotland. During his time at Amazon, he specialized in Display Advertising, where he honed his expertise in building and optimizing deep learning models within world-scale data systems, all while ensuring low latencies for optimal user experience. Viraat's academic journey includes obtaining a Master's in Artificial Intelligence from the prestigious School of Informatics at the University of Edinburgh.

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

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