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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.

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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 122
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Optimal pricing for maximum profit using Amazon SageMaker

AWS Machine Learning

The repricing ML model is a Scikit-Learn Random Forest implementation in SageMaker Script Mode, which is trained using data available in the S3 bucket (the analytics layer). The price recommendations generated by the Lambda predictions optimizer are submitted to the repricing API, which updates the product price on the marketplace.

Scripts 109
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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 70
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Amazon Comprehend Targeted Sentiment adds synchronous support

AWS Machine Learning

Today, we’re excited to announce the new synchronous API for targeted sentiment in Amazon Comprehend, which provides a granular understanding of the sentiments associated with specific entities in input documents. The Targeted Sentiment API provides the sentiment towards each entity.

APIs 76
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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. In Part 1, we focus on creating accurate and reliable agents.

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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 83