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

AWS Machine Learning

With the advancements in automation and configuring with increasing levels of abstraction to set up different environments with IaC tools, the AWS CDK is being widely adopted across various enterprises. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account. Navigate to the AWS Cloud9 console.

Scripts 73
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Its AI/ML solutions drive enhanced operational efficiency, productivity, and customer experience for many of their enterprise clients. Model training: Using the SageMaker SDK, this step runs training code with the respective model image and trains datasets from pre-processing scripts while generating the trained model artifacts.

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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

The majority of enterprise customers already have a well-established MLOps practice with a standardized environment in place—for example, a standardized repository, infrastructure, and security guardrails—and want to extend their MLOps process to no-code and low-code AutoML tools as well. Choose the file browser icon view the path.