Make sure you saw this link for preprocessing first

Training and deploying model with Personalize involves following steps:

1. Creating solution: The solution contains the configurations to train a model.
2. Creating solution version: The solution version is a trained model with the configuration you selected.
3. Creating campaign: A campaign is an endpoint used to host a solution version and make recommendations to users.

Create the configuration for training a model

When you have your dataset group with data in it, the next step is to create a solution. A solution covers two areas—selecting the model (recipe) and then using your data to train it. You have recipes and a popularity baseline from which to choose. Alternatively, you can use AutoML, which runs your data against each of the available recipes and Amazon Personalize then judges the best recipe based on the accuracy results produced.

Python

import boto3

personalize = boto3.client('personalize')

print ('Creating solution')
response = personalize.create_solution(
    name = "my-personalize-solution",
    datasetGroupArn = "DATASET_GROUP_ARN",
    performAutoML = True)

# Get the solution ARN.
solution_arn = response['solutionArn']

CLI

aws personalize create-solution --name my-personalize-solution \
--minTPS 10 --perform-auto-ml \
--dataset-group-arn $DATASET_GROUP_ARN

Train the model

Python

# Use the solution ARN to create a solution version.
print ('Creating solution version')
response = personalize.create_solution_version(solutionArn = solution_arn)
solution_version_arn = response['solutionVersionArn']

CLI

aws personalize create-solution-version \
  --solution-arn $SOLUTION_ARN

This will take a little while as the optimal recipe is selected, trained and tuned. Once the solution version is ACTIVE, evaluate its performance before proceeding.