AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. in 2012 is now widely referred to as ML’s “Cambrian Explosion.”

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AWS Machine Learning Blog

NOVEMBER 15, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. of persons present’ for the sustainability committee meeting held on 5th April, 2012?

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AWS Machine Learning Blog

APRIL 16, 2024

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. Create database connections The built-in SQL browsing and execution capabilities of SageMaker Studio are enhanced by AWS Glue connections. or later image versions.

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AWS Machine Learning Blog

MAY 10, 2023

The SageMaker extension expects the JupyterLab environment to have valid AWS credentials and permissions to schedule notebook jobs. We discuss the steps for setting up credentials and AWS Identity and Access Management (IAM) permissions later in this post. See Installing or updating the latest version of the AWS CLI for instructions.

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AWS Machine Learning Blog

AUGUST 22, 2023

Usually, if the dataset or model is too large to be trained on a single instance, distributed training allows for multiple instances within a cluster to be used and distribute either data or model partitions across those instances during the training process. Each account or Region has its own training instances.

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phData

MARCH 22, 2024

In this blog, we will review the steps to create Snowflake-managed Iceberg tables with AWS S3 as external storage and read them from a Spark or Databricks environment. Externally Managed Iceberg Tables – An external system, such as AWS Glue , manages the metadata and catalog. These tables support read-only access from Snowflake.

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Cambridge Intelligence

OCTOBER 19, 2023

.” First release: 2017 Format: An open-source, hosted, native, property and RDF graph database Top 3 advantages: Built for cloud – Neptune is fully managed by AWS, meaning you can leave infrastructure challenges, updates, backups and other admin tasks to them.

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Flipboard

MARCH 21, 2025

You can use familiar AWS services for model development, generative AI, data processing, and analyticsall within a single, governed environment. These connections are stored in the AWS Glue Data Catalog (Data Catalog) and registered with Lake Formation, allowing you to create a federated catalog for each available data source.

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DrivenData Labs

MAY 1, 2025

The final sub-models use broad semantic clustering, an ensemble of the provided acoustic features, a Whisper classification fine-tune, and a contrastive Whisper fine-tune, designed to focus the model on identifying features independent of age, gender, and semantic group. Cluster 0 was in English and included many people talking to an Alexa.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Solution overview Implementing the solution consists of the following high-level steps: Set up your environment and the permissions to access Amazon HyperPod clusters in SageMaker Studio. You can now use SageMaker Studio to discover the SageMaker HyperPod clusters, and view cluster details and metrics.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Amazon Bedrock Knowledge Bases provides industry-leading embeddings models to enable use cases such as semantic search, RAG, classification, and clustering, to name a few, and provides multilingual support as well. You can set up the notebook in any AWS Region where Amazon Bedrock Knowledge Bases is available.

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