Webinar: How to Deploy Deep Learning Models to Production

Based on various reports and surveys, only 20% to 30% of trained models find their way to production. Efficiently meeting the business and technical requirements to execute these models in their runtime environments and at scale, has become a complex task.

Considering how critical the inference of DL models is in production, it is vital that the performance of these models meets the specifications of the target hardware. In this webinar, you’ll learn about the best practices for maximizing both resource utilization and performance of deep learning models in production. You’ll be able to use these practices in analyzing more efficiently your challenges and options for deploying DL models.

Watch this 45-minute webinar to learn:

  • What to consider when deploying deep learning models in production
  • Common mistakes in deep learning inference deployment
  • How to improve your resource utilization and models’ performance (on edge devices and cloud computing)
  • How to measure and compare the performance of models’ inference and choosing the right candidate
  • Examples via CLI of implementing these techniques and practices with live results

Interested in learning more about how to successfully deploy your models to production? Book a demo to talk with Deci’s experts.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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