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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Next, focus on building the components of the architecture—such as training and preprocessing scripts—within SageMaker Studio or Jupyter Notebook.

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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.