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Panel Discussion: Challenges in Dataset Quality for Machine Learning

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Thought leaders in data annotation share their ideas and discuss the next big challenges in the field of dataset creation for deep learning.

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This event brings together industry leaders in data annotation from Bosch, Zeiss Meditec, Hyundai MOBIS, MIT-IBM Watson Lab, and the Goethe University in Frankfurt. You will gain insights into the current state of the art in machine learning. Register now and find out which open questions have to be addressed to solve the next big challenges in the field of dataset creation for deep learning.


		Panel Discussion: Challenges in Dataset Quality for Machine Learning image

		Panel Discussion: Challenges in Dataset Quality for Machine Learning image

Over the last decade, deep learning has revolutionized one business vertical after the other. It seems fair to say that one of the fields with the largest progress has been autonomous driving; both academia and industry have created countless breakthroughs. Now, the same concepts are being applied to other fields, such as medical imaging, virtual and augmented reality, remote sensing, optical inspection, and many more.

While deep learning models became more and more sophisticated, it simultaneously became apparent that there are other bottlenecks slowing down progress on important applications. On the one end, sifting through petabytes of data per day and choosing the right subsets for annotation requires complex software and tools. And on the other end, verifying the quality of the annotations becomes difficult to scale.


		Panel Discussion: Challenges in Dataset Quality for Machine Learning image

While many large enterprises already have tackled these challenges, the new bottleneck has become choosing both data distribution and data quality that new models are being trained with. The most common questions revolve around three main topics:

  • How can I verify the correctness of my individual annotations and/or the whole dataset?
  • Is my dataset diverse enough, sampling the data space to touch all decision boundaries with the right weighting?
  • Is my taxonomy defined such that learning becomes easier for my model?

Join the discussion about the Challenges in Dataset Quality for Machine Learning. Hear from industry leaders who share their ideas and discuss the next big challenges in the field of dataset creation for deep learning. Register now!

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Organizer Quality Match

Organizer of Panel Discussion: Challenges in Dataset Quality for Machine Learning

Quality Match improves your machine learning models through optimized datasets with controlled annotation quality.

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