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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 108
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Use Amazon SageMaker Studio with a custom file system in Amazon EFS

AWS Machine Learning

In addition to this, the solution expects that the AWS account in which the template is deployed already has the following configuration and resources: You should have a SageMaker Studio domain. Refer to Creating a trail for your AWS account for additional information. You need to create a mount target for each subnet.

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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 86
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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning

get('Account') identity = boto3.client('sts').get_caller_identity()['Arn'] pip install retrying from urllib.request import urlretrieve import json import os import boto3 import random import time from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, RequestError credentials = boto3.Session().get_credentials()

APIs 98
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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 130
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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 78