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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 122
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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 99
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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 120
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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 135
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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 93
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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 134
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Contact Center Trends 2024: Our Predictions

Fonolo

We live in an era of big data, AI, and automation, and the trends that matter in CX this year begin with the abilities – and pain points – ushered in by this technology. For example, big data makes things like hyper-personalized customer service possible, but it also puts enormous stress on data security.