# Train models


Using Amazon SageMaker Unified Studio, you can train foundation models or custom models. 

Follow these steps to train a foundation model:

1. Sign in to Amazon SageMaker Unified Studio using the link that your administrator gave you.

1. Choose a model to train.

   1. From the main menu, choose **Build**.

   1. From the drop-down menu, choose **Jumpstart Models**.

      The JumpStart page lists the model providers.

   1. Choose a model provider. The page displays the models for that provider.

   1. Under **Action**, choose **Trainable**. The page displays the trainable models for that provider.

   1. From the provider's list of models, choose the model you want to train.

1. From the model details page, choose **Train** to create a training job.

   If the model is pretrained, you can fine-tune the model by adjusting the model parameters.

1. In the **Fine-tuning model** page, update the hyperparameters you want to change.

1. Enter **Submit** to submit the training job. You can view the training job from the **Training jobs** page.

You can also train the model in a Jupyterlab notebook using the SageMaker AI python SDK. 

For more information about training models in JumpStart, see [JumpStart pretrained models](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html) in the *Amazon SageMaker AI Developer Guide*.