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Achieve Machine Learning Hyper-Productivity with Transformers and Hugging Face

On Demand

Type

  • Session

Format

  • In-Person

Track

  • Data Science, Machine Learning and MLOps

Difficulty

  • Intermediate

Room

  • Moscone South | Upper Mezzanine | 151

Duration

  • 35 min
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Overview

According to the latest State of AI report, "transformers have emerged as a general-purpose architecture for ML. Not just for Natural Language Processing, but also Speech, Computer Vision or even protein structure prediction." Indeed, the Transformer architecture has proven very efficient on a wide variety of Machine Learning tasks. But how can we keep up with the frantic pace of innovation? Do we really need expert skills to leverage these state-of-the-art models? Or is there a shorter path to creating business value in less time?



In this code-level talk, we'll gradually build and deploy a demo involving several Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, how they can help you become hyper-productive in order to deliver high-quality Machine Learning solutions faster than ever before.

Session Speakers

Julien Simon

Chief Evangelist

Hugging Face

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