From the course: Machine Learning with SageMaker by Pearson
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Model registry demonstration - Amazon SageMaker Tutorial
From the course: Machine Learning with SageMaker by Pearson
Model registry demonstration
Usually, once we have our models created, we need to start tracking them as they sort of evolve over time. And that's where model registry comes in, as we just learned in the previous lesson. So let me show you how to use this relatively quickly. I'm going to use a model that's already been created in one of the previous lessons. Here in xgboost-training, and then this epoch timestamp, I have the output directory, and then model.tar.gz. And we are going to do this in SageMaker Studio. That's where Model Registry is. Let's open up Studio. Also, I do have the instructions opened up here. So step by step, what is SageMaker Model Registry? We just covered that. We need a pre-trained model. We're going to create a model group. register that model, track and manage model versions, prove it for deployment, deploy it, run some inference, and then look at what if we want to iterate, what if we want to add a new version. So let's run through this real quick. Here in SageMaker Studio, you go to…
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Learning objectives39s
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Model evaluation metrics: Accuracy, precision, and recall9m 39s
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Using SageMaker Clarify for bias detection and interpretability7m 40s
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Comparing model performance using A/B testing5m 47s
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Model A/B testing demonstration6m 26s
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Managing model versions with SageMaker Model Registry5m 55s
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Model registry demonstration10m 48s
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