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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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

AWS Machine Learning

Our solution describes an AWS DeepRacer environment configuration using the AWS CDK to accelerate the journey of users experimenting with SageMaker log analysis and reinforcement learning on AWS for an AWS DeepRacer event. Choose Open Jupyter to start running the Python script for performing the log analysis.

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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

You can also add your own Python scripts and transformations to customize workflows. For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference. You can access the testing script from the local path of the code repository that we cloned earlier.