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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. Run the script init-script.bash : chmod u+x init-script.bash./init-script.bash

APIs 118
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Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

AWS Machine Learning

BERT is pre-trained on masking random words in a sentence; in contrast, during Pegasus’s pre-training, sentences are masked from an input document. The model then generates the missing sentences as a single output sequence using all the unmasked sentences as context, creating an executive summary of the document as a result.

Scripts 95
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

For example, a use case that’s been moved from the QA stage to pre-production could be rejected and sent back to the development stage for rework because of missing documentation related to meeting certain regulatory controls. These stages are applicable to both use case and model stages. To get started, set-up a name for your experiment.

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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

Batch transform The batch transform pipeline consists of the following steps: The pipeline implements a data preparation step that retrieves data from a PrestoDB instance (using a data preprocessing script ) and stores the batch data in Amazon Simple Storage Service (Amazon S3).

Scripts 116
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock automates synchronization of your data with your vector store, including diffing the data when it’s updated, document loading, and chunking, as well as semantic embedding. RAG is a popular technique that combines the use of private data with large language models (LLMs). txt) Markdown (.md)

APIs 132
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Build a custom UI for Amazon Q Business

AWS Machine Learning

Amazon Q returns the response as a JSON object (detailed in the Amazon Q documentation ). sourceAttributions – The source documents used to generate the conversation response. In Retrieval Augmentation Generation (RAG), this always refers to one or more documents from enterprise knowledge bases that are indexed in Amazon Q.

APIs 129
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

Scripts 86