Sign in to the AWS console
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Step 1. Paste the hash login URL associated to your name in a browser
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Step 2. Click “AWS Console”, then “Open AWS Console”
Open Amazon SageMaker Studio
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Step 3. Navigate to Amazon SageMaker service
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Step 4. Create Amazon SageMaker Studio session in Oregon (us-west-2) region.
Select "Quick start" with "Create a new role" in the "Execution Role" section, then click "Submit". This will take about 2-3 mins.
Once its completed and the Amazon SageMaker Studio domain has status "Ready", select "Open Studio".
Clone GitHub repository within the Amazon SageMaker Studio
- Step 5. Within Amazon SageMaker Studio, go to the
File → New → Terminal
and then run following command
git clone https://github.com/veerathp/sagemaker-workshop-labs.git
Use Python 3 (Data Science) kernel to run all labs below into SageMaker Studio. For SageMaker notebook instances use conda_python3.
Build, Train and Deploy Machine Learning models using Amazon SageMaker / DeerAR
- Open and run the Notebook Instances in Amazon SageMaker Studio located in
/01-DeepAR/deepar_chicago_traffic_violations.ipynb
Hyper Parameter Optimization using Amazon SageMaker. The first lab will take about 20-30mins to run, once completed move to the second notebook to analyzing the training jobs.
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Open and run the Notebook Instances in Amazon SageMaker Studio located in
/04-sagemaker-hpo-xgboost/hpo_xgboost_direct_marketing_sagemaker_python_sdk.ipynb
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Cont. Open and run the Notebook Instances in Amazon SageMaker Studio located in
/04-sagemaker-hpo-xgboost/HPO_Analyze_TuningJob_Results.ipynb
. Make sure you replace the name of the training job
Running experiments and manage multiple trials
- Open and run the Notebook Instance in Amazon SageMaker Studio located in
/02-customer-churn/xgboost_customer_churn_studio.ipynb
Build, Train and Deploy Machine Learning models for anomaly detection
- Open and run the Notebook Instance in Amazon SageMaker Studio located in
/03-unsupervised-random-cut-forest/random_cut_forest.ipynb
Deploying Machine Learning models using Multi Model endpoints
- Open and run the Notebook Instance in Amazon SageMaker Studio located in
/06-sagemaker-mme/xgboost_multi_model_endpoint_home_value.ipynb