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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. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account.

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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

You can also add your own Python scripts and transformations to customize workflows. Prerequisites This walkthrough includes the following prerequisites: An AWS account. For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.