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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

By the end of the consulting engagement, the team had implemented the following architecture that effectively addressed the core requirements of the customer team, including: Code Sharing – SageMaker notebooks enable data scientists to experiment and share code with other team members.

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

AWS Machine Learning

Now we have low-code and no-code tools like Amazon SageMaker Data Wrangler , AWS Glue DataBrew , and Amazon SageMaker Canvas to assist with data feature engineering. However, a lot of these processes are still currently done manually by a data engineer or analyst who analyzes the data using these tools.