- Use cases
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1. Preprocessing
- SageMaker Object Detection preprocessing
- Rekognition Object Detection preprocessing
- SageMaker Kmeans preprocessing
- Autopilot preprocessing
- DeepAR preprocessing
- Personalize preprocessing
- Select, drop or extract Columns
- Split dataset to Train and Test
- Upload to s3
- Forecast preprocessing
- Rekognition Classification preprocessing
- SageMaker Image Classification preprocessing
- Xgboost preprocessing
- Blazingtext preprocessing
- Comprehend custom preprocessing
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2. Training
- SageMaker Object Detection training
- Rekognition Object Detection training
- Forecast training
- Personalize training
- BlazingText training
- DeepAR training
- SageMaker Kmeans training
- Comprehend custom training
- Autopilot Training
- Xgboost Training
- Autogluon training
- Rekognition Classification training
- SageMaker Image Classification training
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3. Inference
- SageMaker Object Detection inference
- Forecast inference
- Rekognition Object Detection inference
- Comprehend custom inference
- Personalize inference
- Autopilot Inference
- BlazingText Inference
- Custom SageMaker model Inference
- DeepAR Inference
- Rekognition Classification inference
- SageMaker Image Classification inference
- SageMaker Kmeans inference
- Xgboost Inference
- Contribute a use case or contact us for help.
- Frequently Asked Questions
Personalize inference
Make sure you saw this link for training first
Create a Campaign
If you’re happy with the model you created, you can create a campaign in order to deploy it. A campaign is used to make recommendations for your users. You create a campaign by deploying a solution version.
python
response = personalize.create_campaign(
name = 'my-personalize-campaign',
solutionVersionArn = 'solution_version_arn',
minProvisionedTPS = 10)
campaign_arn = response['campaignArn']
CLI
aws personalize create-campaign --name my-personalize-campaign \
--solution-arn $SOLUTION_ARN --update-mode AUTO
After you have created your campaign, use it to make recommendations.