# Tutorial for building models with Notebook Instances
<a name="gs-console"></a>

This Get Started tutorial walks you through how to create a SageMaker notebook instance, open a Jupyter notebook with a preconfigured kernel with the Conda environment for machine learning, and start a SageMaker AI session to run an end-to-end ML cycle. You'll learn how to save a dataset to a default Amazon S3 bucket automatically paired with the SageMaker AI session, submit a training job of an ML model to Amazon EC2, and deploy the trained model for prediction by hosting or batch inferencing through Amazon EC2. 

This tutorial explicitly shows a complete ML flow of training the XGBoost model from the SageMaker AI built-in model pool. You use the [US Adult Census dataset](https://archive.ics.uci.edu/ml/datasets/adult), and you evaluate the performance of the trained SageMaker AI XGBoost model on predicting individuals' income.
+ [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) – The [XGBoost](https://xgboost.readthedocs.io/en/latest/) model is adapted to the SageMaker AI environment and preconfigured as Docker containers. SageMaker AI provides a suite of [built-in algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) that are prepared for using SageMaker AI features. To learn more about what ML algorithms are adapted to SageMaker AI, see [Choose an Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/algorithms-choose.html) and [Use Amazon SageMaker Built-in Algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html). For the SageMaker AI built-in algorithm API operations, see [First-Party Algorithms](https://sagemaker.readthedocs.io/en/stable/algorithms/index.html) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable).
+ [Adult Census dataset](https://archive.ics.uci.edu/ml/datasets/adult) – The dataset from the [1994 Census bureau database](http://www.census.gov/en.html) by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). The SageMaker AI XGBoost model is trained using this dataset to predict if an individual makes over \$150,000 a year or less.

**Topics**
+ [Create an Amazon SageMaker Notebook Instance for the tutorial](gs-setup-working-env.md)
+ [Create a Jupyter notebook in the SageMaker notebook instance](ex1-prepare.md)
+ [Prepare a dataset](ex1-preprocess-data.md)
+ [Train a Model](ex1-train-model.md)
+ [Deploy the model to Amazon EC2](ex1-model-deployment.md)
+ [Evaluate the model](ex1-test-model.md)
+ [Clean up Amazon SageMaker notebook instance resources](ex1-cleanup.md)