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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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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. The following diagram illustrates the solution architecture. The steps are as follows: Open AWS Cloud9 on the console.

Scripts 73
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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference. The Step Functions state machine, S3 bucket, Amazon API Gateway resources, and Lambda function codes are stored in the GitHub repo. The following figure illustrates our Step Function workflow.

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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. For this post, you use a CloudFormation template.