- 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
Autopilot Inference
Make sure you saw this link for preprocessing first
Get the best Candidate job
best_candidate = sm.describe_auto_ml_job(AutoMLJobName=auto_ml_job_name)['BestCandidate']
best_candidate_name = best_candidate['CandidateName']
where auto_ml_job_name is the name of the AutoML job that you used for training.
Create a model for hosting
model_arn = sm.create_model(Containers=best_candidate['InferenceContainers'],
ModelName='your-model-name',
ExecutionRoleArn=role)
Create endpoint configuration and endpoint
ep_config = sm.create_endpoint_config(EndpointConfigName = 'your-endpoint-config-name',
ProductionVariants=[{'InstanceType': 'ml.m5.2xlarge',
'InitialInstanceCount': 1,
'ModelName': 'your-model-name',
'VariantName': 'main'}])
create_endpoint_response = sm.create_endpoint(EndpointName='your-endpoint-name',
EndpointConfigName='your-endpoint-config-name')
Obtain predictions from endpoint
Assuming you have a pandas dataframe called test_data, you can do:
prediction = predictor.predict(test_data.to_csv(sep=',', header=False, index=False)).decode('utf-8')
Related content:
- ☞ Autopilot Training – 1 min read
- ☞ Autogluon training – 2 min read
- ☞ Autopilot preprocessing – 1 min read