Remove Auto-classification Remove Data Quality Remove Software Development
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Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

In this section, we demonstrate how to perform feature engineering on the data from Snowflake using SageMaker Data Wrangler’s built-in capabilities. You can use the report to help you clean and process your data. For Analysis type , choose Data Quality and Insights Report. Choose Create.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices.

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Operationalizing knowledge for data-centric AI

Snorkel AI

This is a platform that supports this new data-centric development loop. This is then used to train models, and those models then power feedback and analyses that guide how to improve the quality of your data and therefore of your models. This could be something really simple.

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Operationalizing knowledge for data-centric AI

Snorkel AI

This is a platform that supports this new data-centric development loop. This is then used to train models, and those models then power feedback and analyses that guide how to improve the quality of your data and therefore of your models. This could be something really simple.