Learning with Limited Labeled Data with Shioulin Sam

EPISODE 255
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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's Hybrid Data Platform offers a single, unified platform to power data and machine learning workflows across any private, public, multi, or hybrid-cloud deployment. CDP Machine Learning helps organizations build, deploy, and scale ML and AI applications through a repeatable, industrialized approach that streamlines the process of getting analytic workloads into production and intelligently manages machine learning use cases across the business, at scale. CDP Machine Learning lets enterprise data science teams collaborate across the full data lifecycle–with immediate access to enterprise data pipelines, scalable compute resources, and their preferred tools–without compromising the agility, security, and governance needs of the business.

To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml!

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