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

AWS Machine Learning

In this post, we enable the provisioning of different components required for performing log analysis using Amazon SageMaker on AWS DeepRacer via AWS CDK constructs. This is where advanced log analysis comes into play. Choose Open Jupyter to start running the Python script for performing the log analysis.

Scripts 73
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro has used the input filter and join functionality of SageMaker batch transformation API. The response is returned to Lambda and sent back to the application through API Gateway. Use QuickSight refresh dataset APIs to automate the spice data refresh. Implement group-based security for dashboard and analysis access control.

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

AWS Machine Learning

Additionally, we won’t be able to make an informed decision post-analysis of those insights prior to building the ML models. This solution can accelerate accurate and timely inspection of data and model quality checks, and facilitate the productivity of distinguished data and ML teams across your organization. Overview of solution.

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Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

AWS Machine Learning

Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. The neural forecasters can be bundled as a single ensemble model, or incorporated individually into Bosch’s model universe, and accessed easily via REST API endpoints.

APIs 82