Tag: Amazon SageMaker
Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering | Amazon Web Services
The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume...
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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...
Meta Llama 3 models are now available in Amazon SageMaker JumpStart | Amazon Web Services
Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. The Llama...
Slack delivers native and secure generative AI powered by Amazon SageMaker JumpStart | Amazon Web Services
This post is co-authored by Jackie Rocca, VP of Product, AI at Slack
Slack is where work...
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...
Distributed training and efficient scaling with the Amazon SageMaker Model Parallel and Data Parallel Libraries | Amazon Web Services
There has been tremendous progress in the field of distributed deep learning for large language models (LLMs), especially after the release of ChatGPT in...
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...
AWS at NVIDIA GTC 2024: Accelerate innovation with generative AI on AWS | Amazon Web Services
AWS was delighted to present to and connect with over 18,000 in-person and 267,000 virtual attendees at NVIDIA GTC, a global artificial intelligence (AI)...
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...
Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat | Amazon Web Services
Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications,...
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...
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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