From the course: Learning Amazon SageMaker AI
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Training a model - Amazon SageMaker Tutorial
From the course: Learning Amazon SageMaker AI
Training a model
- [Instructor] Now let's walk through training a model using one of SageMaker's built-in algorithms. For this example, we'll use XGBoost and Python code to predict traffic congestion levels in Dataville. Let's open JupyterLab There are a few prerequisites. Remember the notebook and flow files we downloaded from Data Wrangler? You'll want to ensure you've uploaded those to JupyterLab and executed all the cells in the notebook. You'll want to note the feature store or feature group name here. You'll need that for training. If you encounter an access denied for repository error scroll down, looking for two variables, the container URI and the container URI pinned, and make sure that these values are accurate. Now let's navigate to the training notebook. I've executed all the cells in the notebook to save time. Now let's look at each cell. Scroll down. In that feature group or feature store name, make sure you copy and paste that here with your correct one. Next up, query the feature…
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