- 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 Object Detection training
Make sure you've seen this if you need help creating a dataset first! If you already have a usable dataset, follow along here to train a model:
Create a training job
Navigate to SageMaker console and click on “Training jobs”. Once there, click on “Create training job”.
In the job details, add the job name, create a new IAM role or use an existing role which has necessary permission. Select “Object Detection” algorithm from the drop down and use “Pipe” as the Input mode.
Continuing on training job details, put appropriate instance type and maximum runtime.
In the hyperparameter selection section, make sure to put correct number of training samples.
Edit input data configuration section and make sure to put S3 location for the output.manifest file that was created from Ground Truth labeling job.
Create another channel named “validation” and fill in similar details as test channel.
At last, specify the S3 path to save output model artifacts. Click on “Create training job”
You will see the training job in progress on your SageMaker console.
Once the training job is finished successfully, you will see the status as “Completed”. We will use the output artifacts to create a deployable model, click on “Create model”.
In the create model section, add model name and verify the auto-populated details. Click on “Create model”.
Once the model is created, you can click on “Models” from SageMaker console and see its summary
Related content:
- ☞ SageMaker Object Detection inference – 1 min read
- ☞ SageMaker Object Detection preprocessing – 2 min read
- ☞ Autogluon training – 2 min read