Create Regression or Classification Jobs for Tabular Data Using the AutoML API

For tabular data, the set of algorithms run on your data to train your model candidates is dependent on your modeling strategy (ENSEMBLING or HYPERPARAMETER_TUNING). The following details how to set this training mode.

If you keep blank (or null), the Mode is inferred based on the size of your dataset.

For information on Autopilot's stacked ensembles and hyperparameters optimization training methods, see Training modes and algorithm support

Features selection

Autopilot provides automatic data-preprocessing steps including feature selection and feature extraction. However, you can manually provide the features to be used in training with the FeatureSpecificatioS3Uri attribute.

Selected features should be contained within a JSON file in the following format:

{ "FeatureAttributeNames":["col1", "col2", ...] }

The values listed in ["col1", "col2", ...] are case sensitive. They should be a list of strings containing unique values that are subsets of the column names in the input data.

Algorithms selection

By default, your Autopilot job runs a pre-defined list of algorithms on your dataset to train model candidates. The list of algorithms depends on the training mode (ENSEMBLING or HYPERPARAMETER_TUNING) used by the job.

You can provide a subset of the default selection of algorithms.

For the list of available algorithms per training Mode, see AutoMLAlgorithms. For details on each algorithm, see Training modes and algorithm support.

You can provide your own validation dataset and custom data split ratio, or let Autopilot split the dataset automatically.

For information on split and cross-validation in Autopilot see Cross-validation in Autopilot.

You can add a sample weights column to your tabular dataset and then pass it to your AutoML job to request dataset rows to be weighted during training and evaluation.

Support for sample weights is available in ensembling mode only. Your weights should be numeric and non-negative. Data points with invalid or no weight value are excluded. For more information on the available objective metrics, see Autopilot weighted metrics.

You can configure your AutoML job V2 to automatically initiate a remote job on Amazon EMR Serverless when additional compute resources are needed to process large datasets. By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you. EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB.

To allow your AutoML job V2 to automatically transition to EMR Serverless for large dataset, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN field, to the AutoMLComputeConfig of the AutoML job V2 input request.

The ExecutionRoleARN is the ARN of the IAM role granting the AutoML job V2 the necessary permissions to run EMR Serverless jobs.

This role should have the following trust relationship:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "emr-serverless.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

And grant the permissions to:

The IAM policy defined in the provided JSON document grants those permissions:

{
    "Version": "2012-10-17",
    "Statement": [{
+            "Sid": "EMRServerlessCreateApplicationOperation",
+            "Effect": "Allow",
+            "Action": "emr-serverless:CreateApplication",
+            "Resource": "arn:aws:emr-serverless:*:*:/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:RequestTag/sagemaker:is-canvas-resource": "True",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "EMRServerlessListApplicationOperation",
+            "Effect": "Allow",
+            "Action": "emr-serverless:ListApplications",
+            "Resource": "arn:aws:emr-serverless:*:*:/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "EMRServerlessApplicationOperations",
+            "Effect": "Allow",
+            "Action": [
+                "emr-serverless:UpdateApplication",
+                "emr-serverless:GetApplication"
+            ],
+            "Resource": "arn:aws:emr-serverless:*:*:/applications/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:ResourceTag/sagemaker:is-canvas-resource": "True",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "EMRServerlessStartJobRunOperation",
+            "Effect": "Allow",
+            "Action": "emr-serverless:StartJobRun",
+            "Resource": "arn:aws:emr-serverless:*:*:/applications/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:RequestTag/sagemaker:is-canvas-resource": "True",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "EMRServerlessListJobRunOperation",
+            "Effect": "Allow",
+            "Action": "emr-serverless:ListJobRuns",
+            "Resource": "arn:aws:emr-serverless:*:*:/applications/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:ResourceTag/sagemaker:is-canvas-resource": "True",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "EMRServerlessJobRunOperations",
+            "Effect": "Allow",
+            "Action": [
+                "emr-serverless:GetJobRun",
+                "emr-serverless:CancelJobRun"
+            ],
+            "Resource": "arn:aws:emr-serverless:*:*:/applications/*/jobruns/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:ResourceTag/sagemaker:is-canvas-resource": "True",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "EMRServerlessTagResourceOperation",
+            "Effect": "Allow",
+            "Action": "emr-serverless:TagResource",
+            "Resource": "arn:aws:emr-serverless:*:*:/*",
+            "Condition": {
+                "StringEquals": {
+                    "aws:RequestTag/sagemaker:is-canvas-resource": "True",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
+        },
+        {
+            "Sid": "IAMPassOperationForEMRServerless",
+            "Effect": "Allow",
+            "Action": "iam:PassRole",
+            "Resource": "arn:aws:iam::*:role/EMRServerlessRuntimeRole-*",
+            "Condition": {
+                "StringEquals": {
+                    "iam:PassedToService": "emr-serverless.amazonaws.com",
+                    "aws:ResourceAccount": "${aws:PrincipalAccount}"
+                }
+            }
         }
    ]
}