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Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach

Flipboard

The end-to-end workflow features a supervisor agent at the center, classification and conversion agents branching off, a humanintheloop step, and Amazon Simple Storage Service (Amazon S3) as the final unstructured data lake destination. Make sure that every incoming data eventually lands, along with its metadata, in the S3 data lake.

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Unlock the value of your Azure data with Tableau

Tableau

we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere. March 30, 2021.

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An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

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Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Big Data Architect.

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Best 8 Data Version Control Tools for Machine Learning 2024

DagsHub

Released in 2022, DagsHub’s Direct Data Access (DDA for short) allows Data Scientists and Machine Learning engineers to stream files from DagsHub repository without needing to download them to their local environment ahead of time. This can prevent lengthy data downloads to the local disks before initiating their mode training.

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Introducing the Amazon Comprehend flywheel for MLOps

AWS Machine Learning Blog

This feature also allows you to automate model retraining after new datasets are ingested and available in the flywheel´s data lake. Data lake – A flywheel’s data lake is a location in your Amazon Simple Storage Service (Amazon S3) bucket that stores all its datasets and model artifacts. Choose Create job.

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Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

Foundation models (FMs) on Amazon Bedrock provide powerful generative models for text and language tasks. Modify the stack name or leave as default, then choose Next. In the Parameters section, input the Amazon Cognito user pool ID ( CognitoUserPoolId ) and application client ID ( CognitoAppClientId ).

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Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel

AWS Machine Learning Blog

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. Periodically, the new labeled data (to retrain the model) can be made available to flywheel by creating datasets. One for the data lake for Comprehend flywheel.