- 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
SageMaker Kmeans inference
Make sure you read this on training a model with Kmeans before deploying your model to an endpoint.
Deploying your Kmeans model is as simple as:
kmeans_predictor = kmeans.deploy(initial_instance_count=1,
instance_type='ml.t2.medium')
Once the endpoint is deployed, you can use your train data and determine which cluster each training row belongs to:
result=kmeans_predictor.predict(train_data)
Related content:
- ☞ SageMaker Kmeans training – 2 min read
- ☞ SageMaker Kmeans preprocessing – 3 min read