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Our solution describes an AWS DeepRacer environment configuration using the AWS CDK to accelerate the journey of users experimenting with SageMaker log analysis and reinforcement learning on AWS for an AWS DeepRacer event. Choose Open Jupyter to start running the Python script for performing the log analysis.
Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. Implement group-based security for dashboard and analysis access control.
This notebook contains everything you need to run the transformations over our historical dataset and ingest the resulting features into Feature Store.This notebook uses Feature Store to create a feature group , runs your Data Wrangler flow on the entire dataset using a SageMaker processing job, and ingests the processed data to Feature Store.
You can also add your own Python scripts and transformations to customize workflows. Model groups This tab lists groups of model versions that were created by pipeline runs in the project. You can choose the model group to access the latest version of the model. Choose the file browser icon view the path.
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