Case Study

WSC Sports Video Platform Reduces Cloud Cost by 78%

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Discover How Deci Achieved 4.6x Acceleration in Model Throughput, Without Compromising Accuracy

WSC Sports is a well-established sports video platform and technology company providing AI-based video solutions to global media and streaming companies. WSC Sport’s highly experienced data science team builds and uses many complex deep learning models for their line of products. The company was seeing high costs for one of their novel deep learning applications in the cloud, due to the large number of GPUs needed for production.

In this case study you will learn about:

  • The intense computing demands and costs associated with deep learning inference
  • How you can meet performance and cost requirements for deep learning inference at scale
  • What it takes for you to automatically optimize inference performance with AutoNAC technology, without compromising the model’s accuracy

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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