Remove 2012 Remove Big Data Analytics Remove Data Preparation
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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 Blog

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*" elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

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

AWS Machine Learning Blog

Both the training and validation data are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket for model training in the client account, and the testing dataset is used in the server account for testing purposes only. Details of the data preparation code are in the following notebook.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. He develops and codes cloud native solutions with a focus on big data, analytics, and data engineering.

Python 123