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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

By using the Livy REST APIs , SageMaker Studio users can also extend their interactive analytics workflows beyond just notebook-based scenarios, enabling a more comprehensive and streamlined data science experience within the Amazon SageMaker ecosystem. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*" elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

Big data 116
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How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances

AWS Machine Learning

The challenge The VirtuSwap Minerva engine creates recommendations for optimal distribution of liquidity between different liquidity pools, while taking into account multiple parameters, such as trading volumes, current market liquidity, and volatilities of traded assets, constrained by a total amount of liquidity available for distribution.

APIs 141
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Detect and protect sensitive data with Amazon Lex and Amazon CloudWatch Logs

AWS Machine Learning

At the same time, it’s crucial to make sure these security measures don’t undermine the functionality and analytics critical to business operations. Sensitive data, such as name, account number, and phone number, should be tagged with a high classification level, indicating the need for stringent security measures.

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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning

With a background in AI/ML, data science, and analytics, Yunfei helps customers adopt AWS services to deliver business results. He designs AI/ML and data analytics solutions that overcome complex technical challenges and drive strategic objectives. About the authors Yunfei Bai is a Senior Solutions Architect at AWS.

APIs 102
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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

To achieve that, AWS offers a unified modern data platform that is powered by Amazon Simple Storage Service (Amazon S3) as the data lake with purpose-built tools and processing engines to support analytics and ML workloads. To complete this tutorial, you must have the following prerequisites: Have an AWS account. Prerequisites.

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning

However, sometimes due to security and privacy regulations within or across organizations, the data is decentralized across multiple accounts or in different Regions and it can’t be centralized into one account or across Regions. Each account or Region has its own training instances.

Scripts 98
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Fireside Chat: Natero Shares Tips for Customer Success

Satrix Solutions

Natero helps Customer Success Managers reduce churn, increase expansion, and manage more accounts. Read our interview: Evan Klein: How has the Customer Success industry changed since Natero was first founded in 2012? For example, high-value accounts can command more face time and personal attention. Customer data silos.

SaaS 60