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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Complete the following steps: Choose Prepare and analyze data.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations.

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Deploying Conversational AI Products to Production With Jason Flaks

The MLOps Blog

We strive to do that, but sometimes you run into a corner where you have no choice but to really get quality results you have to do that. And even on the operation side of things, is there a separate operations team, and then you have your research or ml engineers doing these pipelines and stuff? Data quality is critical.

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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. Create a model package group in the ML Shared Services account (Account A) and share it with the ML Dev account (Account B).

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