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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. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

Together, these additions help agronomists, software developers, ML engineers, data scientists, and remote sensing teams provide scalable, valuable decision-making support systems to farmers. These differences in satellite images and frequencies also lead to differences in API capabilities and features.

APIs 76
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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

References More information is available at the following resources: Automate Amazon SageMaker Studio setup using AWS CDK AWS SageMaker CDK API reference About the Authors Zdenko Estok works as a cloud architect and DevOps engineer at Accenture.

Scripts 75
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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.

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Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

AWS Machine Learning

Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. The neural forecasters can be bundled as a single ensemble model, or incorporated individually into Bosch’s model universe, and accessed easily via REST API endpoints.

APIs 84
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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

Data Wrangler provides an end-to-end solution to import, prepare, transform, featurize, and analyze data. You can integrate a Data Wrangler data preparation flow into your ML workflows to simplify and streamline data preprocessing and feature engineering using little to no coding. For this post, you use a CloudFormation template.