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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

In this post, we address the analytics and ML platform team as a consumer in the data mesh. The platform team sets up the ML environment for the data scientists and helps them get access to the necessary data products in the data mesh. When querying data via Athena, the intermediate results are also saved in Amazon S3.

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Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning

The steps involved are as follows: The financial analyst poses questions via a platform such as chatbots. The platform uses a framework to determine the most suitable multi-modal agent tool to answer the question. Once identified, the platform runs the code that is linked to the previously identified tool.

Marketing 103
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Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

AWS Machine Learning

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. In this post, we cover the benefits for SaaS platforms to integrate with SageMaker, the range of possible integrations, and the process for developing these integrations.

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Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

For more information, refer to How JPMorgan Chase built a data mesh architecture to drive significant value to enhance their enterprise data platform. We address data consumption by the analytics and ML CoE with Amazon Athena and Amazon SageMaker in part 2 of this series. Create the Athena workgroup consumer-workgroup.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning

Solution overview With SageMaker Studio JupyterLab notebook’s SQL integration, you can now connect to popular data sources like Snowflake, Athena, Amazon Redshift, and Amazon DataZone. Create an Athena connection Athena is a fully managed SQL query service from AWS that enables analysis of data stored in Amazon S3 using standard SQL.

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What are AWS Data Lakes?

ConvergeOne

A Data Lake provides organizations with a centralized repository for a wide variety of data forms, located in a central platform that supports structured, semi-structured, and unstructured data. Services like EMR can run your dig data applications or Amazon Athena for ad-hoc real-time interactive analytics. Basically, it is a mess!

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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

For users who are not familiar with the service offerings that are provided on the cloud platform, they may provide different generic ways and descriptions when searching for such a diagram. The following figure shows an example diagram that illustrates an orchestrated extract, transform, and load (ETL) architecture solution.