AWS Machine Learning Blog

NOVEMBER 22, 2023

Yes, the AWS re:Invent season is upon us and as always, the place to be is Las Vegas! are the sessions dedicated to AWS DeepRacer ! Generative AI is at the heart of the AWS Village this year. You marked your calendars, you booked your hotel, and you even purchased the airfare. And last but not least (and always fun!)

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AWS Machine Learning Blog

NOVEMBER 12, 2024

You may check out additional reference notebooks on aws-samples for how to use Meta’s Llama models hosted on Amazon Bedrock. You can implement these steps either from the AWS Management Console or using the latest version of the AWS Command Line Interface (AWS CLI). Solutions Architect at AWS. Varun Mehta is a Sr.

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phData

NOVEMBER 8, 2024

Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It can also be integrated into major data platforms like Snowflake. Contact phData Today!

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AWS Machine Learning Blog

JUNE 13, 2023

Overall, implementing a modern data architecture and generative AI techniques with AWS is a promising approach for gleaning and disseminating key insights from diverse, expansive data at an enterprise scale. AWS also offers foundation models through Amazon SageMaker JumpStart as Amazon SageMaker endpoints.

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AWS Machine Learning Blog

JANUARY 4, 2024

In this pattern, we use Retrieval Augmented Generation using vector embeddings stores, like Amazon Titan Embeddings or Cohere Embed , on Amazon Bedrock from a central data catalog, like AWS Glue Data Catalog , of databases within an organization. In entered the Big Data space in 2013 and continues to explore that area.

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Pickl AI

FEBRUARY 23, 2025

Amazon EMR (Elastic MapReduce) Amazon EMR is a cloud-native Big Data platform that simplifies running Big Data frameworks such as Apache Hadoop and Apache Spark on AWS. Statistics : According to AWS reports, EMR reduces the time required for Big Data processing tasks by up to 90% compared to traditional methods.

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