Learning with Limited Labeled Data with Shioulin Sam

Banner Image: Shioulin Sam - Podcast Interview
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About this Episode

Today, in the first episode of our Strata Data conference series, we're joined by Shioulin Sam, Research Engineer with Cloudera Fast Forward Labs. Shioulin and I caught up to discuss the newest report to come out of CFFL, "Learning with Limited Label Data," which explores active learning as a means to build applications requiring only a relatively small set of labeled data. We start our conversation with a review of active learning and some of the reasons why it's recently become an interesting technology for folks building systems based on deep learning. We then discuss some of the differences between active learning approaches or implementations, and some of the common requirements of an active learning system. Finally, we touch on some packaged offerings in the marketplace that include active learning, including Amazon's SageMaker Ground Truth, and review Shoulin's tips for getting started with the technology.
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Thanks to our sponsor Cloudera

Cloudera delivers an enterprise data cloud for any data, anywhere, from the Edge to AI. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world's largest enterprises.
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