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Architect a mature generative AI foundation on AWS

Flipboard

Data quality is ownership of the consuming applications or data producers. Governance The two key areas of governance are model and data: Model governance Monitor model for performance, robustness, and fairness. Bharathi Srinivasan is a Generative AI Data Scientist at the AWS Worldwide Specialist Organization.

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New projects contribute to digital commons

Hacker News

The OpenEPT database infrastructure will facilitate collaboration between engineers and researchers by promoting data exchange. We will reassess and improve the performance of Verilog behavioural models and revise the mixed mode simulation algorithm.

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McKinsey QuantumBlack on automating data quality remediation with AI

Snorkel AI

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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McKinsey QuantumBlack on automating data quality remediation with AI

Snorkel AI

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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McKinsey QuantumBlack on automating data quality remediation with AI

Snorkel AI

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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Prepare your data for Amazon Personalize with Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

Users Amazon Personalize need to upload data containing their own customer’s interactions in order for the model to be able to learn these behavioral trends. Choose the plus sign next to the final step on the data flow and choose Add analysis. For Analysis type ¸ choose Data Quality And Insights Report for Amazon Personalize.

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Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

AWS Machine Learning Blog

One of the challenges of working with categorical data is that it is not as amenable to being used in many machine learning algorithms. To overcome this, we use one-hot encoding, which converts each category in a column to a separate binary column, making the data suitable for a wider range of algorithms.

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