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Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit

AWS Machine Learning

Instead of hardcoding the custom function into your custom transform step, you pull a script containing the function from CodeCommit, load it, and call the loaded function in your custom transform step. The data is related to the direct marketing campaigns of a banking institution. The following diagram illustrates this solution.

Scripts 62
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Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS

AWS Machine Learning

Model weights are available via scripts in the GitHub repository , and the MSAs are hosted by the Registry of Open Data on AWS (RODA). We use aws-do-eks , an open-source project that provides a large collection of easy-to-use and configurable scripts and tools to enable you to provision EKS clusters and run your inference.

APIs 77
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Connecting Amazon Redshift and RStudio on Amazon SageMaker

AWS Machine Learning

Users can also interact with data with ODBC, JDBC, or the Amazon Redshift Data API. If you’d like to use the traditional SageMaker Studio experience with Amazon Redshift, refer to Using the Amazon Redshift Data API to interact from an Amazon SageMaker Jupyter notebook. The CloudFormation script created a database called sagemaker.

APIs 104
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Enhancing AWS intelligent document processing with generative AI

AWS Machine Learning

In addition to existing capabilities, businesses need to summarize specific categories of information, including debit and credit data from documents such as financial reports and bank statements. In the current scenario, you need to dedicate resources to accomplish such tasks using human review and complex scripts.

APIs 75
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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

The data scientists in this team use Amazon SageMaker to build and train a credit risk prediction model using the shared credit risk data product from the consumer banking LoB. The processing job queries the data via Athena and uses a script to split the data into training, testing, and validation datasets.

Scripts 71
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

In the context of banking, they might deduce statistical insights from account balances, identifying trends and flow patterns. The second script accepts the AWS RAM invitations to discover and access cross-account feature groups from the owner level. The hurdle they often face is redundancy.

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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Text classification.

Scripts 72