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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.

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Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning

Deutsche Bahn is a leading transportation organization in Germany with a revenue of 56.3 They offer a wide range of services, including public and regional transport, freight services, and rail infrastructure. They offer a wide range of services, including public and regional transport, freight services, and rail infrastructure.

APIs 127
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Get smarter search results with the Amazon Kendra Intelligent Ranking and OpenSearch plugin

AWS Machine Learning

Amazon Kendra Intelligent Ranking application programming interface (API) – The functions from this API are used to perform tasks related to provisioning execution plans and semantic re-ranking of your search results. Create and start OpenSearch using the Quickstart script. Download the search_processing_kendra_quickstart.sh

Scripts 102
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 136
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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

An administrator can run the AWS CDK script provided in the GitHub repo via the AWS Management Console or in the terminal after loading the code in their environment. With the help of the AWS CDK, we can version control our provisioned resources and have a highly transportable environment that complies with enterprise-level best practices.

Scripts 92
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Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

AWS Machine Learning

For more information, refer to Using the SageMaker Python SDK and Using the Low-Level SageMaker APIs. After you have defined the instance groups, you need to modify your training script to read the SageMaker training environment information that includes heterogeneous cluster configuration. The launcher.py

Scripts 88
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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning

Best practices We discuss the following best practices in this post: Compute – SageMaker Training is a great API to launch CPU dataset preparation jobs and thousand-scale GPU jobs. The SageMaker jobs APIs, namely SageMaker Training and SageMaker Processing, excel for this type of tasks.