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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

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. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

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. About the Authors Stephen Randolph is a Senior Partner Solutions Architect at Amazon Web Services (AWS).

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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. This walkthrough includes the following prerequisites: An AWS account. Otherwise, your account may hit the service quota limits of running an m5.4x

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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

You can also add your own Python scripts and transformations to customize workflows. Prerequisites This walkthrough includes the following prerequisites: An AWS account. You can access the testing script from the local path of the code repository that we cloned earlier. Choose the file browser icon view the path.