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GTC ON-DEMAND

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Abstract:
Learn from real-world case studies where large corpora of unstructured data were indexed and organized by deep-learning pipelines. Organizations are capturing and saving exponentially more unstructured data. As a tactic to organize this data, many teams turn to manual data classification, but that human-in-the-loop process can be cost prohibitive and introduce metadata inaccuracies. By applying deep learning and cluster-based labeling, we can index petabyte-scale datasets and rapidly organize unstructured data for downstream model building and analysis. This session will teach you how to quickly switch to training on all the contents of your data lake, rather than just a subset. We will use cases studies with real-world datasets to walk through best practices for a deep learning indexing pipeline.
Learn from real-world case studies where large corpora of unstructured data were indexed and organized by deep-learning pipelines. Organizations are capturing and saving exponentially more unstructured data. As a tactic to organize this data, many teams turn to manual data classification, but that human-in-the-loop process can be cost prohibitive and introduce metadata inaccuracies. By applying deep learning and cluster-based labeling, we can index petabyte-scale datasets and rapidly organize unstructured data for downstream model building and analysis. This session will teach you how to quickly switch to training on all the contents of your data lake, rather than just a subset. We will use cases studies with real-world datasets to walk through best practices for a deep learning indexing pipeline.  Back
 
Topics:
Accelerated Data Science, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8962
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