AWS Machine Learning Blog

OCTOBER 5, 2023

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

You can import data directly through over 50 data connectors such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Snowflake, and Salesforce. In this walkthrough, we will cover importing your data directly from Snowflake. You can download the dataset loans-part-1.csv Product Manager at AWS.

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AWS Machine Learning Blog

MAY 10, 2023

In this post, we show how you can publish predictive dashboards in QuickSight using ML-based predictions from Canvas, without explicitly downloading predictions and importing into QuickSight. You can copy the prediction by choosing Copy , or download it by choosing Download prediction.

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AWS Machine Learning Blog

JUNE 3, 2024

based single sign-on (SSO) methods, such as AWS IAM Identity Center. To learn more, see Secure access to Amazon SageMaker Studio with AWS SSO and a SAML application. To learn more about importing data to SageMaker Canvas, see Import data into Canvas. Choose Import data , then choose Tabular.

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Flipboard

MARCH 22, 2023

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. For more information about prerequisites, see Get Started with Data Wrangler.

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Flipboard

MARCH 7, 2023

You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI). The Studio Image Build CLI lets you build SageMaker-compatible Docker images directly from your Studio environments by using AWS CodeBuild. Environments without internet access.

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Mlearning.ai

JANUARY 26, 2023

Most important to note about ARCO is that, unlike data systems from a decade ago, modern data cubes should ideally be Cloud-native (meaning: ready for fast and efficient web-services / scalable applications / API’s) and pre-processed so that they can be directly used for modelling and eventually for decision-making. Data, 4(3), 92.

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