Tag: Amazon S3
Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint | Amazon Web Services
Speaker diarization, an essential process in audio analysis, segments an audio file based on speaker identity. This post delves into integrating Hugging Face’s PyAnnote...
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Integrate HyperPod clusters with Active Directory for seamless multi-user login | Amazon Web Services
Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training...
Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average | Amazon Web Services
We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is...
Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock – Part 2 | Amazon Web Services
In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a...
Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock | Amazon Web Services
See CHANGELOG for latest features and fixes.
You’ve likely experienced the challenge of taking notes during a...
Uncover hidden connections in unstructured financial data with Amazon Bedrock and Amazon Neptune | Amazon Web Services
In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking...
Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks | Amazon Web Services
Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process...
Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model | Amazon Web Services
Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and...
Build an active learning pipeline for automatic annotation of images with AWS services | Amazon Web Services
This blog post is co-written with Caroline Chung from Veoneer.
Veoneer is a global automotive electronics company...
Knowledge Bases for Amazon Bedrock now supports metadata filtering to improve retrieval accuracy | Amazon Web Services
At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely...
Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers | Amazon Web Services
In January 2024, Amazon SageMaker launched a new version (0.26.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs). This version offers support for...
Improving Content Moderation with Amazon Rekognition Bulk Analysis and Custom Moderation | Amazon Web Services
Amazon Rekognition makes it easy to add image and video analysis to your applications. It’s based on the same proven, highly scalable, deep learning...
Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities | Amazon Web Services
This is a guest post co-authored by Shravan Kumar and Avirat S from Gramener.
Gramener, a Straive...
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