Category: Amazon SageMaker

Today, we are excited to announce that the Jina Embeddings v2 model, developed by Jina AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running model inference. This state-of-the-art model supports an impressive 8,192-tokens context length. You can deploy this model with SageMaker JumpStart, a machine learning (ML) hub […]

In today’s fast-paced corporate landscape, employee mental health has become a crucial aspect that organizations can no longer overlook. Many companies recognize that their greatest asset lies in their dedicated workforce, and each employee plays a vital role in collective success. As such, promoting employee well-being by creating a safe, inclusive, and supportive environment is […]

Today, we are excited to announce that the first model in the next generation Falcon 2 family, the Falcon 2 11B foundation model (FM) from Technology Innovation Institute (TII), is available through Amazon SageMaker JumpStart to deploy and run inference. Falcon 2 11B is a trained dense decoder model on a 5.5 trillion token dataset […]

Large multimodal models (LMMs) integrate multiple data types into a single model. By combining text data with images and other modalities during training, multimodal models such as Claude3, GPT-4V, and Gemini Pro Vision gain more comprehensive understanding and improved ability to process diverse data types. The multimodal approach allows models to handle a wider range […]

Mixture of Experts (MoE) architectures for large language models (LLMs) have recently gained popularity due to their ability to increase model capacity and computational efficiency compared to fully dense models. By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per […]

This post shows you how to predict domain-specific product attributes from product images by fine-tuning a VLM on a fashion dataset using Amazon SageMaker, and then using Amazon Bedrock to generate product descriptions using the predicted attributes as input. So you can follow along, we’re sharing the code in a GitHub repository.

In this post, we explore a solution that addresses these challenges head-on using LoRA serving with Amazon SageMaker. By using the new performance optimizations of LoRA techniques in SageMaker large model inference (LMI) containers along with inference components, we demonstrate how organizations can efficiently manage and serve their growing portfolio of fine-tuned models, while optimizing costs and providing seamless performance for their customers. The latest SageMaker LMI container offers unmerged-LoRA inference, sped up with our LMI-Dist inference engine and OpenAI style chat schema. To learn more about LMI, refer to LMI Starting Guide, LMI handlers Inference API Schema, and Chat Completions API Schema.

Today, we are excited to announce the Mixtral-8x22B large language model (LLM), developed by Mistral AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models so you […]

This post is co-written with HyeKyung Yang, Jieun Lim, and SeungBum Shim from LotteON. LotteON aims to be a platform that not only sells products, but also provides a personalized recommendation experience tailored to your preferred lifestyle. LotteON operates various specialty stores, including fashion, beauty, luxury, and kids, and strives to provide a personalized shopping […]

Amazon Ads helps advertisers and brands achieve their business goals by developing innovative solutions that reach millions of Amazon customers at every stage of their journey. At Amazon Ads, we believe that what makes advertising effective is delivering relevant ads in the right context and at the right moment within the consumer buying journey. With that […]