Create a Regression or Classification Autopilot experiment for tabular data using the Studio Classic UI

To create an Autopilot experiment using Studio Classic UI

1. Sign in at https://console.aws.amazon.com/sagemaker/, choose Studio from the left navigation pane, select your Domain and user profile, then Open Studio.

2. In Studio, choose the Studio Classic icon in the top left navigation pane. This opens a Studio Classic app.

3. Run or open a Studio Classic application from the space of your choice, or Create Studio Classic space. . On the Home tab, choose the AutoML card. This opens a new AutoML tab.

4. Choose Create an AutoML experiment. This opens a new Create experiment tab.

5. In the Experiment and data details section, enter the following information:

   1. Experiment name – Must be unique to your account in the current AWS Region and contain a maximum of 63 alphanumeric characters. Can include hyphens (-) but not spaces.

   2. Input data – Provide the Amazon Simple Storage Service (Amazon S3) bucket location of your input data. This S3 bucket must be in your current AWS Region. The URL must be in an s3:// format where Amazon SageMaker AI has write permissions. The file must be in CSV or Parquet format and contain at least 500 rows. Select Browse to scroll through available paths and Preview to see a sample of your input data.

   3. Is your S3 input a manifest file? – A manifest file includes metadata with your input data. The metadata specifies the location of your data in Amazon S3. It also specifies how the data is formatted and which attributes from the dataset to use when training your model. You can use a manifest file as an alternative to preprocessing when your labeled data is being streamed in Pipe mode.

   4. Auto split data? – Autopilot can split your data into an 80-20% split for training and validation data. If you prefer a custom split, you can choose the Specify split ratio. To use a custom dataset for validation, choose Provide a validation set.

   5. Output data location (S3 bucket) – The name of the S3 bucket location where you want to store the output data. The URL for this bucket must be in an Amazon S3 format where Amazon SageMaker AI has write permissions. The S3 bucket must be in the current AWS Region. Autopilot can also create this for you in the same location as your input data.

6. Choose Next: Target and features. The Target and features tab opens.

7. In the Target and features section:

   • Select a column to set as a target for model predictions.

   • Optionally, you can pass the name of a sample weights column in the Sample weight section to request your dataset rows to be weighted during training and evaluation. For more information on the available objective metrics, see Autopilot weighted metrics.

     Note

     Support for sample weights is available in ensembling mode only.

   • You can also select features for training and change their data type. The following data types are available: Text, Numerical, Categorical, Datetime, Sequence, and Auto. All features are selected by default.

8. Choose Next: Training method. The Training method tab opens.

9. In the Training method section, select your training option: Ensembling, Hyperparameter optimization (HPO), or Auto to let Autopilot choose the training method automatically based on the dataset size. Each training mode runs a pre-defined set of algorithms on your dataset to train model candidates. By default, Autopilot pre-selects all the available algorithms for the given training mode. You can run an Autopilot training experiment with all the algorithms or choose your own subset.

   For more information on the training modes and the available algorithms, see the Autopilot training modes section in the Training modes and algorithms page.

10. Choose Next: Deployment and advanced settings to open the Deployment and advanced settings tab. Settings include the auto-display endpoint name, machine learning problem type, and additional choices for running your experiment.

    1. Deployment settings – Autopilot can automatically create an endpoint and deploy your model for you.

       To auto-deploy to an automatically generated endpoint, or to provide an endpoint name for custom deployment, set the toggle to Yes under Auto deploy? If you are importing data from Amazon SageMaker Data Wrangler, you have additional options to auto-deploy the best model with or without the transforms from Data Wrangler.

       Note

       If your Data Wrangler flow contains multi-row operations such as groupby, join, or concatenate, you can't auto-deploy with these transforms. For more information, see Automatically Train Models on Your Data Flow.

    2. Advanced settings (optional) – Autopilot provides additional controls to manually set experimental parameters such as defining your problem type, time constraints on your Autopilot job and trials, security, and encryption settings.

       Note

       Autopilot supports the setting of default values to simplify the configuration of Autopilot experiments using Studio Classic UI. Administrators can use Studio Classic lifecycle configurations (LCC) to set infrastructure, networking, and security values in configuration files and pre-populate the advanced settings of AutoML jobs.

       To learn about how administrators can automate the customization of an Autopilot experiment, see Configure the default parameters of an Autopilot experiment (for administrators).

       1. Machine learning problem type – Autopilot can automatically infer the type of supervised learning problem from your dataset. If you prefer to choose it manually, you can use the Select the machine learning problem type dropdown menu. Note that it defaults to Auto. In some cases, SageMaker AI is unable to infer accurately. When that happens, you must provide the value for the job to succeed. In particular, you can choose from the following types:

          • Binary classification– Binary classification assigns input data to one of two predefined and mutually exclusive classes, based on their attributes, such as medical diagnosis based on results of diagnostic tests that determine if someone has a disease.

          • Regression – Regression establishes a relationship between the input variables (also known as independent variables or features) and the target variable (also known as the dependent variable). This relationship is captured through a mathematical function or model that maps the input variables to a continuous output. It is commonly used for tasks such as predicting house prices based on features like square footage and the number of bathrooms, stock market trends, or estimating sales figures.

          • Multiclass classification – Multiclass classification assigns input data to one of several classes based on their attributes, like the prediction of the topic most relevant to a text document, such as politics, finance, or philosophy.

       2. Runtime – You can define a maximum time limit. Upon reaching the time limit, trials and jobs that exceed the time constraint automatically stop.

       3. Access – You can choose the role that Amazon SageMaker Studio Classic assumes to gain temporary access to AWS services (in particular, SageMaker AI and Amazon S3) on your behalf. If no role is explicitly defined, Studio Classic automatically uses the default SageMaker AI execution role attached to your user profile.

       4. Encryption – To enhance the security of your data at rest and protect it against unauthorized access, you can specify encryption keys to encrypt data in your Amazon S3 buckets and in the Amazon Elastic Block Store (Amazon EBS) volume attached to your Studio Classic domain.

       5. Security – You can choose the virtual private cloud (Amazon VPC) in which your SageMaker AI job runs. Ensure that the Amazon VPC has access to your input and output Amazon S3 buckets.

       6. Project – Specify the name of the SageMaker AI project to associate with this Autopilot experiment and model outputs. When you specify a project, Autopilot tags the project to an experiment. This lets you know which model outputs are associated with this project.

       7. Tags – Tags are an array of key-value pairs. Use tags to categorize your resources from AWS services, such as their purpose, owner, or environment.

    3. Choose Next: Review and create to get a summary of your Autopilot experiment before you create it.

11. Select Create experiment.The creation of the experiment starts an Autopilot job in SageMaker AI. Autopilot provides the status of the experiment, information on the data exploration process and model candidates in notebooks, a list of generated models and their reports, and the job profile used to create them.

    For information on the notebooks generated by an Autopilot job, see Autopilot notebooks generated to manage AutoML tasks. For information on the details of each model candidate and their reports, see Share an Autopilot model with a SageMaker Canvas user.