Deep Learning Model Deployment
Let's say you have just finished training a deep learning model. Perhaps it is a model for computer vision, or perhaps for natural language processing. You have adjusted the hyperparamete...
Throughout this course, we will work with a convolutional neural network that classifies chest X-rays to detect pneumonia. It's a pre-trained model with around 88% accuracy, and we'll take it from a saved checkpoint to a deployed service capable of processing real prediction queries.
You trained your model in PyTorch or TensorFlow. Now you need to save it for deployment. This is where things get interesting because the format you choose depends on two things: the framework you trained in, and where you deploy.
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What's inside
6 sections- 1 Table of Contents
- 2 Prepare models for production
- 3 Deploy models as services
- 4 Monitor and maintain models in production
- 5 Appendix: – Complete Code Files
- 6 Full deployment flow summary
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