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AMA: How to Productize Deep Learning Models

Model development is a long process. The initial stages include preparing your data, selecting the architecture, and training your model. But even after you’re done with all these steps, there’s still a long way to go before your model is productized and the application is up and running.

The road to product deployment is paved with different challenges. Now is the time to raise any questions and get answers from the industry’s leading deep learning experts.

Moderated by Ofer Baratz, Deep Learning Product Manager, together with Sefi Kligler, VP of AI, and Lotem Fridman, Director of Engineering, you will in the AMA:

  • Have an opportunity to share your difficulties in productizing your deep learning models
  • Discover best practices for deep learning models deployment
  • Learn how industry leaders are dealing with deployment challenges

After watching the webinar, you can go ahead and build, optimize, and deploy your models using the free community version of Deci’s deep learning platform.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")